Experiment 3: Document at various level for datasets(COVID-science : non-science : Psychology)

1. Article level

#import
import os.path
from gensim import corpora
from gensim.models import LsiModel
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from gensim.models.coherencemodel import CoherenceModel
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import sklearn as s
import dataframe_image as dfi
import networkx as nx
import mantel

C:\ProgramData\Anaconda3\lib\site-packages\gensim\similarities\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.
  warnings.warn(msg)

covid-science.txt

def load_data(path,file_name,seperator):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    document = ''
    i=0
    with open( os.path.join(path, file_name) ,"r", encoding="utf8") as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for line in fin.readlines():
            text = line.strip()
            i=i+1
            if text !='':
                document = document+text
                if text == seperator:
                    documents_list.append(document)
                    document = ''
        documents_list.append(document)
    print("Total Number of Documents:",len(documents_list))
    titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count_science=load_data("","covid-science.txt",'NEW_PAPER')

Number of words in text file : 120257
Total Number of Documents: 20

def preprocess_data(doc_set):
    """
    Input  : docuemnt list
    Purpose: preprocess text (tokenize, removing stopwords, and stemming)
    Output : preprocessed text
    """
    # initialize regex tokenizer
    tokenizer = RegexpTokenizer(r'\w+')
    # create English stop words list
    en_stop = set(stopwords.words('english'))
    # Create p_stemmer of class PorterStemmer
    p_stemmer = PorterStemmer()
    # list for tokenized documents in loop
    texts = []
    # loop through document list
    for i in doc_set:
        # clean and tokenize document string
        raw = i.lower()
        tokens = tokenizer.tokenize(raw)
        tokens = [i for i in tokens if len(i)>4]
        print(tokens)
        # remove stop words from tokens
        stopped_tokens = [i for i in tokens if not i in en_stop]
        # stem tokens
        stemmed_tokens = [p_stemmer.stem(i) for i in stopped_tokens]
        # add tokens to list
        texts.append(stemmed_tokens)
    return texts

clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnetmethodology', 'predicting', 'covid', '19based', 'chest', 'imagess', 'tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herreraabstract', 'currently', 'coronavirus', 'disease', 'covid', 'infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans', 'orchest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'mostmedical', 'centers', 'hence', 'cases', 'imagesbecome', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networkshave', 'great', 'potential', 'building', 'covid', 'triage', 'systemsand', 'detecting', 'covid', 'patients', 'especially', 'patients', 'withlow', 'severity', 'unfortunately', 'current', 'databases', 'allowbuilding', 'systems', 'highly', 'heterogeneousand', 'biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved', 'bymost', 'recent', 'covid', 'classification', 'models', 'under', 'aclose', 'collaboration', 'hospital', 'universitario', 'clínico', 'sancecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levelsmanuscript', 'received', 'september', 'revised', 'october', 'accepted', 'november', 'publication', 'november', 'current', 'version', 'december', 'supported', 'bythe', 'project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundaciónbbva', 'equipos', 'investigación', 'científica', 'covid', '192020', 'spanish', 'ministry', 'science', 'technology', 'under', 'theproject', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajalprogramme', '18136', 'gómez', 'supported', 'thefpu', 'programme', 'fpu16', '04765', 'charte', 'supported', 'fpuprogramme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'researchcouncil', 'grant', 'agreement', '647038', 'biodesert', 'projectis', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'andalusian', 'research', 'institute', 'indata', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'hospital', 'universitario', 'clínicosan', 'cecilio', 'granada', '36310', 'spain', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'atlanttic', 'research', 'center', 'telecommunication', 'technologies', 'university', 'galicia', 'spain', 'mreyarea', 'gmail', 'guirado', 'multidisciplinary', 'institute', 'environmentstudies', 'ramón', 'margalef', 'university', 'alicante', '03690', 'spain', 'geesecillo', 'gmail', 'digital', 'object', 'identifier', '3037127of', 'severity', 'normal', 'positive', 'moderate', 'severe', 'covidgr', 'contains', 'positive', 'and426', 'negative', 'posteroanterior', 'views', 'wepropose', 'covid', 'smart', 'based', 'network', 'covid', 'sdnet', 'methodology', 'improving', 'generalization', 'capacity', 'ofcovid', 'classification', 'models', 'approach', 'reaches', 'goodand', 'stable', 'results', 'accuracy', 'severe', 'moderate', 'andmild', 'covid', 'severity', 'levels', 'approach', 'could', 'inthe', 'early', 'detection', 'covid', 'covidgr', 'along', 'withthe', 'severity', 'level', 'labels', 'available', 'scientific', 'community', 'through', 'https', 'dasci', 'transferencia', 'covidgr', 'index', 'terms', 'covid', 'convolutional', 'neural', 'networks', 'smart', 'introductionin', 'months', 'world', 'witnessing', 'howcovid', 'pandemic', 'increasingly', 'infecting', 'large', 'massof', 'people', 'everywhere', 'world', 'trends', 'arenot', 'clear', 'research', 'confirm', 'problem', 'maypersist', 'until', 'besides', 'prevalence', 'studies', 'conducted', 'inseveral', 'countries', 'reveal', 'proportion', 'populationhave', 'developed', 'antibodies', 'after', 'exposure', 'virus', 'spain', 'means', 'frequently', 'large', 'number', 'patientswill', 'assessed', 'small', 'intervals', 'numberof', 'clinicians', 'resources', 'general', 'covid', 'diagnosis', 'carried', 'using', 'leastone', 'these', 'three', 'tests', 'computed', 'tomography', 'scans', 'based', 'assessment', 'itconsists', 'analyzing', 'radiographic', 'images', 'different', 'angles', 'needed', 'equipment', 'assessmentis', 'available', 'hospitals', 'takes', 'than15', 'minutes', 'patient', 'addition', 'required', 'forct', 'decontamination', '2reverse', 'transcription', 'polymerase', 'chain', 'reaction', 'rtpcr', 'detects', 'viral', 'sputum', 'online', 'available', 'https', 'english', 'elpais', 'society', 'antibody', 'study', 'shows', 'spaniards', 'contracted', 'coronavirus', 'online', 'available', 'advocacy', 'economics', 'acrposition', 'statements', 'recommendations', 'chest', 'radiography', 'forsuspected', 'covid19', 'infection', 'article', 'access', 'download', 'along', 'rights', 'mining', 'analysis', 'authorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'xplore', 'restrictions', 'apply', 'journal', 'biomedical', 'health', 'informatics', 'december', '2020fig', 'stratification', 'radiological', 'severity', 'covid', 'examples', 'index', 'calculated', 'nasopharyngeal', 'requires', 'specific', 'material', 'andequipment', 'which', 'easily', 'accessible', 'takes', 'atleast', 'hours', 'which', 'desirable', 'positive', 'covid19', 'patients', 'should', 'identified', 'tracked', 'possible', 'studies', 'found', 'results', 'severaltests', 'different', 'points', 'patients', 'variable', 'during', 'course', 'illness', 'producing', 'falsenegative', 'authors', 'suggested', 'testshould', 'combined', 'other', 'clinical', 'tests', 'chest', 'required', 'equipment', 'thisassessment', 'cumbersome', 'lightweightand', 'transportable', 'general', 'resources', 'moreavailable', 'required', 'tests', 'addition', 'takes', 'about', 'seconds', 'patient', 'which', 'makes', 'effectiveassessment', 'tools', 'recent', 'studies', 'provide', 'estimates', 'expert', 'radiologistssensitivity', 'diagnosis', 'covid', 'based', 'scans', 'study', 'patients', 'chestct', 'essay', 'performed', 'within', 'reported', 'asensitivity', 'compared', 'sensitivityof', 'different', 'study', 'patients', 'age56', 'years', 'reported', 'sensitivity', 'comparedwith', 'initial', 'according', 'analysis', '636ambulatory', 'patients', 'patients', 'presenting', 'urgent', 'carecenters', 'confirmed', 'coronavirus', 'disease', 'normal', 'ormildly', 'abnormal', 'findings', 'these', 'patientsare', 'correctly', 'diagnosed', 'expert', 'recent', 'study', 'authors', 'proposed', 'simplifying', 'quantification', 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'dlmodel', 'could', 'serve', 'triage', 'method', 'support', 'formedical', 'decision', 'making', 'increasing', 'number', 'recent', 'worksclaim', 'achieving', 'impressive', 'sensitivities', 'higher', 'thanexpert', 'radiologists', 'these', 'sensitivities', 'biasin', 'covid', 'dataset', 'covid', 'image', 'datacollection', 'dataset', 'includes', 'small', 'number', 'ofcovid', 'positive', 'cases', 'coming', 'highly', 'heterogeneoussources', 'least', 'countries', 'cases', 'severe', 'patients', 'issue', 'drastically', 'reduces', 'clinical', 'value', 'populatenon', 'covid', 'healthy', 'classes', 'researchers', 'usingcxr', 'images', 'diverse', 'pulmonary', 'disease', 'repositories', 'theobtained', 'models', 'clinical', 'value', 'since', 'theywill', 'unable', 'detect', 'patients', 'moderate', 'severity', 'which', 'target', 'clinical', 'triage', 'system', 'thissituation', 'there', 'still', 'higher', 'quality', 'datasets', 'builtunder', 'clinical', 'protocol', 'under', 'close', 'collaborationwith', 'expert', 'radiologists', 'multiple', 'studies', 'proven', 'higher', 'quality', 'ensureshigher', 'quality', 'models', 'concept', 'smart', 'refers', 'tothe', 'process', 'converting', 'higher', 'quality', 'datawith', 'higher', 'concentration', 'useful', 'information', 'smart', 'dataincludes', 'processing', 'methods', 'improve', 'value', 'andveracity', 'examples', 'these', 'methods', 'include', 'noiseelimination', 'augmentation', 'transformation', 'among', 'other', 'techniques', 'designed', 'clinical', 'quality', 'dataset', 'named', 'covidgr', 'includes', 'levels', 'severity', 'normal', 'moderate', 'severe', 'identified', 'thesefour', 'severity', 'levels', 'recent', 'covid', 'radiological', 'study', 'propose', 'covid', 'smart', 'based', 'network', 'covid', 'sdnet', 'methodology', 'combines', 'segmentation', 'dataaugmentation', 'transformations', 'together', 'appropriate', 'convolutional', 'neural', 'network', 'inference', 'contributions', 'paper', 'summarized', 'follows', 'analyze', 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'fully', 'automated', 'because', 'accuracyof', 'parsing', 'extracting', 'cannot', 'always', 'guaranteedby', 'using', 'current', 'packages', 'hence', 'users', 'intervenein', 'quality', 'control', 'process', 'image', 'typedata', 'product', 'quality', 'therapid', 'development', 'parsing', 'images', 'keepupdating', 'component', 'minimize', 'human', 'interventionin', 'automation', 'process']

def prepare_corpus(doc_clean):
    """
    Input  : clean document
    Purpose: create term dictionary of our courpus and Converting list of documents (corpus) into Document Term Matrix
    Output : term dictionary and Document Term Matrix
    """
    # Creating the term dictionary of our courpus, where every unique term is assigned an index. dictionary = corpora.Dictionary(doc_clean)
    dictionary = corpora.Dictionary(doc_clean)
    # Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.
    doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]
    # generate LDA model
    return dictionary,doc_term_matrix

def create_gensim_lsa_model(doc_clean,number_of_topics,words):
    """
    Input  : clean document, number of topics and number of words associated with each topic
    Purpose: create LSA model using gensim
    Output : return LSA model
    """
    dictionary,doc_term_matrix=prepare_corpus(doc_clean)
    # generate LSA model
    lsamodel = LsiModel(doc_term_matrix, num_topics=number_of_topics, id2word = dictionary)  # train model
    #print(lsamodel.print_topics(num_topics=number_of_topics, num_words=words))
    return lsamodel

# LSA Model
number_of_topics=20
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_covid_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_science_articles = create_gensim_lsa_model(clean_text,number_of_topics,words)

dict(model_science.show_topic(0, topn=5))

{'covid': 0.5195000201940337,
 'imag': 0.3819419924959858,
 'model': 0.24414357176752965,
 'dataset': 0.17846130209695152,
 'class': 0.15297059818817454}

from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph12.png')

# LSA Model

a=pd.DataFrame()

for i in range(number_of_topics):
    words_covid_science =dict(model_science.show_topic(i, topn=words))
    b=pd.DataFrame(words_covid_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled15.png')

cosine-similarity
             covid       imag       model     dataset      class
covid            1   0.114718   0.0699065  -0.0721506   0.275795
imag      0.114718          1   -0.101632    0.179926  0.0364016
model    0.0699065  -0.101632           1  0.00335218   0.049745
dataset -0.0721506   0.179926  0.00335218           1  0.0568222
class     0.275795  0.0364016    0.049745   0.0568222          1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig15.png')

covid-non-science.txt

document_list,titles,word_count_nscience=load_data("","covid-non-science.txt",'NEW NEWSPAPER')

Number of words in text file : 8713
Total Number of Documents: 20

clean_text=preprocess_data(document_list)

['covid', 'recovery', 'covid', 'realityby', 'reuterspublished', 'updated', '2020by', 'joneslondon', 'reuters', 'world', 'shares', 'inched', 'towards', 'month', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'nagging', 'coronavirus', 'nerves', 'market', 'rally', 'fuelled', 'record', 'numbers', 'largely', 'blown', 'itself', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'decade', 'stimulus', 'ensured', 'optimism', 'remained', 'chinese', 'shares', 'charged', 'their', 'highest', 'level', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'traders', 'surprise', 'currency', 'commodity', 'markets', 'subdued', 'after', 'otherwise', 'strong', 'confidence', 'sensitive', 'stalwarts', 'copper', 'sterling', 'australian', 'dollar', 'which', 'struggled', 'friday', 'think', 'infection', 'rates', 'fears', 'localised', 'lockdowns', 'doused', 'enthusiasm', 'societe', 'generale', 'strategist', 'jukes', 'three', 'elements', 'vaccine', 'hopes', 'decent', 'places', 'return', 'infection', 'rates', 'which', 'nervous', 'against', 'basket', 'currencies', 'dollar', 'slightly', 'early', 'london', 'trading', 'still', 'firmly', 'track', 'biggest', 'weekly', 'since', 'first', 'though', 'gained', 'against', 'swiss', 'franc', 'versus', 'sometimes', 'commodity', 'driven', 'norwegian', 'crown', 'futures', 'volumes', 'lower', 'usual', 'markets', 'holiday', 'friday', 'independence', 'nonfarm', 'payrolls', 'surged', 'million', 'above', 'average', 'forecast', 'million', 'thanks', 'rises', 'hospitality', 'sectors', 'economists', 'noted', 'there', 'caveats', 'upbeat', 'headline', 'figures', 'number', 'permanent', 'losers', 'continued', 'increasing', 'million', 'while', 'unemployment', 'remains', 'chunky', 'percentage', 'points', 'above', 'february', 'level', 'deutsche', 'analysis', 'unemployment', 'behind', 'developed', 'market', 'peers', 'barring', 'canada', 'recovery', 'faces', 'headwinds', 'surge', 'coronavirus', 'infections', 'prompts', 'states', 'delay', 'cases', 'reverse', 'plans', 'stores', 'reopen', 'activities', 'resume', 'three', 'dozen', 'states', 'increases', 'covid', 'cases', 'cases', 'florida', 'spiking', 'above', 'nevertheless', 'markets', 'largely', 'overlooking', 'spikes', 'taking', 'overall', 'situation', 'still', 'improving', 'overall', 'german', 'government', 'yields', 'basis', 'points', 'their', 'biggest', 'weekly', 'month', 'though', 'nudged', 'friday', 'riskier', 'italian', 'yields', 'though', 'which', 'their', 'lowest', 'since', 'march', 'prices', 'eased', 'after', 'otherwise', 'solid', 'brent', 'crude', 'barrel', 'while', 'crude', 'dropped', 'barrel', 'around', 'months', 'copper', 'prices', 'poised', 'seventh', 'consecutive', 'weekly', 'their', 'longest', 'winning', 'streak', 'nearly', 'three', 'years', 'despite', 'slight', 'easing', 'after', 'supplier', 'chile', 'assured', 'traders', 'about', 'supply', 'three', 'month', 'copper', 'hovering', 'tonne', 'ploughed', 'march', 'issue', 'hangs', 'markets', 'surge', 'secondary', 'infections', 'trigger', 'second', 'national', 'rather', 'regional', 'shutdowns', 'malcolm', 'freeman', 'director', 'kingdom', 'futures', 'wrote', 'reporting', 'jones', 'newspaper']
['covid', 'vaccine', 'frontrunnersby', 'reuterspublished', 'october', 'updated', 'october', '2020oct', 'reuters', 'pfizer', 'could', 'november', 'authorization', 'covid', 'vaccine', 'developing', 'opening', 'possibility', 'vaccine', 'being', 'available', 'united', 'states', 'moderna', 'astrazeneca', 'close', 'behind', 'largest', 'drugmaker', 'likely', 'early', 'their', 'vaccine', 'candidates', 'before', 'potential', 'vaccines', 'being', 'developed', 'tested', 'globally', 'covid', 'pandemic', 'human', 'trials', 'according', 'world', 'health', 'organization', 'following', 'tracks', 'candidates', 'final', 'stages', 'testing', 'separate', 'factbox', 'coronavirus', 'vaccines', 'under', 'development', 'click', 'company', 'stage', 'development', 'expected', 'doses', 'pfizer', 'stage', 'trials', 'underway', 'option', 'biontech', 'pfizer', 'earlier', 'october', 'could', 'emergency', 'authorization', 'november', 'indicating', 'vaccine', 'could', 'potentially', 'available', 'additional', 'canada', 'specified', 'japan', 'moderna', 'completed', 'enrollment', 'option', 'subjects', 'stage', 'trial', 'additional', 'interim', 'results', 'trial', 'expected', 'november', 'japan', 'distributed', 'takeda', 'canada', 'specified', 'johnson', 'johnson', 'large', 'vaccine', 'trial', 'resume', 'after', 'paused', 'under', 'subsequent', 'agreement', 'illness', 'study', 'volunteer', 'results', 'trial', 'expected', 'option', 'early', 'additional', 'canada', 'japan', 'applicable', 'astrazeneca', 'stage', 'trials', 'underway', 'supply', 'deals', 'countries', 'oxford', 'university', 'trial', 'vaccine', 'candidate', 'doses', 'pause', 'since', 'after', 'patient', 'trial', 'results', 'expected', 'coming', 'weeks', 'commitment', 'deliver', 'total', 'least', 'additional', 'italy', 'germany', 'netherlands', 'france', 'additional', 'canada', 'japan', 'sinovac', 'biotech', 'stage', 'trials', 'brazil', 'vaccine', 'approved', 'emergency', 'indonesia', 'turkey', 'underway', 'china', 'supply', 'prelim', 'expected', 'november', 'indonesia', 'least', 'doses', 'before', 'gamaleya', 'research', 'stage', 'trial', 'covid', 'people', 'inoculated', 'institute', 'vaccine', 'sputnik', 'underway', 'russia', 'supply', 'deals', 'since', 'prelim', 'countries', 'including', 'india', 'expected', 'november', 'brazil', 'saudi', 'arabia', 'doses', 'cansino', 'biologics', 'vaccine', 'candidate', 'final', 'stage', 'mexico', 'doses', 'early', 'trials', 'approved', 'chinese', 'military', 'stage', 'trial', 'underway', 'pakistan', 'sinopharm', 'group', 'stage', 'trials', 'expects', 'produce', 'underway', 'billion', 'doses', 'reporting', 'dania', 'nadeem', 'mrinalika', 'bengaluru', 'editing', 'ankur', 'banerjee', 'samuel', 'newspaper']
['giants', 'another', 'covid', 'caseby', 'reuterspublished', 'november', 'updated', 'november', '2020the', 'giants', 'another', 'covid', 'monday', 'night', 'notified', 'player', 'tested', 'positive', 'covid', 'giants', 'announced', 'tuesday', 'morning', 'player', 'immediately', 'isolated', 'contact', 'tracing', 'process', 'initiated', 'close', 'contacts', 'staff', 'members', 'identified', 'informed', 'remain', 'today', 'giants', 'entering', 'their', 'second', 'player', 'positive', 'covid', 'prior', 'playing', 'tampa', 'buccaneers', 'offensive', 'lineman', 'hernandez', 'tested', 'positive', 'hernandez', 'spent', 'nearly', 'weeks', 'reserve', 'covid', 'returned', 'played', 'sunday', 'victory', 'philadelphia', 'eagles', 'field', 'level', 'medianew', 'newspaper']
['covid', 'antivirals', 'taskforce', 'launchedby', 'mediapublished', 'april', 'updated', 'april', '2021at', 'covid', 'treatments', 'being', 'sought', 'government', 'experts', 'speed', 'recovery', 'people', 'become', 'infected', 'virus', 'government', 'taskforce', 'supercharge', 'search', 'antiviral', 'medications', 'covid', 'prime', 'minister', 'boris', 'johnson', 'group', 'medicines', 'covid', 'tracks', 'hoped', 'antivirals', 'could', 'reduce', 'infections', 'limit', 'impact', 'variants', 'protect', 'people', 'cannot', 'vaccines', 'those', 'fully', 'protected', 'after', 'having', 'officials', 'bring', 'treatments', 'online', 'could', 'people', 'covid', 'those', 'close', 'contact', 'could', 'offered', 'antiviral', 'medication', 'offered', 'tablet', 'infection', 'spreading', 'speed', 'recovery', 'antivirals', 'taskforce', 'which', 'appoint', 'chair', 'search', 'promising', 'potential', 'antiviral', 'medicines', 'which', 'taken', 'taskforce', 'support', 'development', 'drugs', 'through', 'clinical', 'trials', 'manufacturing', 'opportunities', 'antiviral', 'drugs', 'medication', 'specifically', 'treating', 'viral', 'infections', 'killing', 'preventing', 'growth', 'viruses', 'success', 'vaccination', 'programme', 'demonstrated', 'achieve', 'bring', 'together', 'brightest', 'minds', 'johnson', 'antivirals', 'taskforce', 'develop', 'innovative', 'treatments', 'covid', 'tracks', 'these', 'could', 'provide', 'another', 'vital', 'defence', 'against', 'future', 'increase', 'infections', 'lives', 'health', 'secretary', 'hancock', 'medicines', 'vital', 'weapon', 'protect', 'loved', 'terrible', 'virus', 'modelled', 'success', 'vaccines', 'therapeutics', 'taskforces', 'which', 'played', 'crucial', 'response', 'pandemic', 'bringing', 'together', 'supercharge', 'search', 'antiviral', 'treatments', 'autumn', 'government', 'chief', 'scientific', 'adviser', 'patrick', 'vallance', 'speed', 'which', 'vaccines', 'therapeutics', 'dexamethasone', 'identified', 'deployed', 'against', 'covid', 'critical', 'pandemic', 'response', 'antivirals', 'tablet', 'another', 'response', 'could', 'protect', 'those', 'protected', 'ineligible', 'vaccines', 'could', 'another', 'layer', 'defence', 'variants', 'concern', 'nikita', 'kanani', 'medical', 'director', 'primary', 'england', 'research', 'reality', 'record', 'speed', 'during', 'pandemic', 'taskforce', 'identify', 'convenient', 'treatments', 'patients', 'covid', 'commenting', 'announcement', 'penny', 'visiting', 'professor', 'pharmaceutical', 'medicine', 'kings', 'college', 'london', 'antiviral', 'treatment', 'influenza', 'shown', 'reduce', 'hospitalisation', 'prevent', 'death', 'epidemic', 'pandemic', 'waves', 'similar', 'preferably', 'simple', 'antiviral', 'suitable', 'community', 'range', 'interventions', 'critical', 'enable', 'remain', 'covid', 'pandemic', 'event', 'emergence', 'viral', 'variants', 'insensitive', 'vaccines', 'there', 'number', 'early', 'phase', 'trials', 'looking', 'antiviral', 'medications', 'newspaper']
['african', 'covid', 'deaths', '000by', 'afppublished', 'february', 'updated', 'february', '2021africa', 'thursday', 'recorded', 'deaths', 'covid', 'milestone', 'likely', 'understate', 'continent', 'billion', 'people', 'battles', 'second', 'infections', 'countries', 'region', 'death', 'reported', 'cases', 'according', 'tally', 'continent', 'relatively', 'spared', 'pandemic', 'except', 'oceania', 'reach', 'threshold', 'deaths', 'which', 'europe', 'crossed', 'april', 'south', 'africa', 'worst', 'african', 'country', 'rolled', 'testing', 'campaign', 'start', 'pandemic', 'country', 'recorded', 'nearly', 'million', 'cases', 'deaths', 'those', 'figures', 'based', 'daily', 'reports', 'communicated', 'health', 'authorities', 'reflect', 'fraction', 'actual', 'health', 'specialists', 'cases', 'clearly', 'under', 'reported', 'because', 'access', 'healthcare', 'facilities', 'under', 'reporting', 'milder', 'cases', 'south', 'african', 'virologist', 'barry', 'schoub', 'member', 'scientific', 'council', 'south', 'african', 'ministry', 'health', 'understaffed', 'health', 'facilities', 'means', 'meant', 'african', 'countries', 'unable', 'testing', 'countries', 'mainly', 'tests', 'capitals', 'further', 'moves', 'urban', 'centres', 'there', 'tests', 'explained', 'french', 'epidemiologist', 'emmanuel', 'baron', 'doctors', 'without', 'borders', 'disease', 'unnoticed', 'asymptomatic', 'patients', 'symptoms', 'confused', 'others', 'added', 'covid', 'found', 'pawpaw', 'zimbabwe', 'country', 'devastated', 'economy', 'mismanaged', 'health', 'system', 'hospitals', 'filled', 'covid', 'patients', 'exhausted', 'doctors', 'overwhelmed', 'nurses', 'official', 'number', 'cases', 'remains', 'tanzania', 'stopped', 'testing', 'after', 'claiming', 'found', 'positive', 'covid', 'pawpaw', 'quail', 'tanzanian', 'government', 'released', 'official', 'figures', 'april', 'someone', 'continent', 'would', 'deaths', 'infection', 'probably', 'would', 'believed', 'nkengasong', 'africa', 'director', 'centers', 'disease', 'control', 'preventions', 'reporters', 'thursday', 'africa', 'however', 'significantly', 'lower', 'europe', 'world', 'affected', 'region', 'deaths', 'recorded', 'other', 'regions', 'badly', 'latin', 'america', 'deaths', 'united', 'states', 'canada', 'deaths', 'after', 'sharp', 'increase', 'january', 'africa', 'figures', 'fallen', 'sharply', 'weeks', 'seven', 'continent', 'recorded', 'deaths', 'percent', 'previous', 'height', 'pandemic', 'january', 'continent', 'deaths', 'disaster', 'while', 'coronavirus', 'figures', 'clearly', 'underestimated', 'health', 'disaster', 'africa', 'baron', 'several', 'studies', 'antibodies', 'which', 'possible', 'detect', 'whether', 'person', 'recovered', 'previously', 'exposed', 'virus', 'underway', 'african', 'countries', 'should', 'provide', 'better', 'impact', 'pandemic', 'region', 'south', 'africa', 'where', 'almost', 'latest', 'cases', 'attributed', 'variant', 'virus', 'known', 'contagious', 'which', 'spread', 'widely', 'represents', 'nearly', 'deaths', 'reported', 'cases', 'continent', 'other', 'african', 'countries', 'affected', 'egypt', 'deaths', 'cases', 'morocco', 'deaths', 'cases', 'south', 'africa', 'country', 'covid', 'fatalities', 'continent', 'counting', 'deaths', 'inhabitants', 'ahead', 'tunisia', 'deaths', 'eswatini', 'lagging', 'behind', 'vaccination', 'continent', 'leading', 'industrial', 'powerhouse', 'administered', 'first', 'vaccines', 'wednesday', 'globally', 'covid', 'caused', 'million', 'infections', 'million', 'deaths', 'since', 'start', 'epidemic', 'wuhan', 'china', 'december', 'harnew', 'newspaper']
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['covid', 'restrictions', 'easing', 'nswby', 'australian', 'associated', 'presspublished', 'september', 'updated', 'september', '2020nsw', 'easing', 'covid', 'restrictions', 'around', 'school', 'community', 'lower', 'community', 'acquired', 'covid', 'numbers', 'weddings', 'people', 'official', 'wedding', 'party', 'dance', 'floor', 'where', 'previously', 'bride', 'groom', 'permittedschools', 'interschool', 'activities', 'resume', 'saturday', 'covid', 'safety', 'school', 'sport', 'return', 'normal', 'parents', 'school', 'sites', 'playing', 'instruments', 'singing', 'chanting', 'permitted', 'strict', 'restrictions', 'formals', 'graduations', 'permitted', 'school', 'formals', 'permitted', 'after', 'kindergarten', 'orientation', 'transition', 'excursions', 'camps', 'resume', 'strict', 'guidelines', 'school', 'choirs', 'musical', 'ensembles', 'resume', 'players', 'metre', 'distance', 'another', 'three', 'metres', 'players', 'reeded', 'woodwind', 'instruments', 'community', 'languages', 'schools', 'program', 'resume', 'learning', 'sport', 'parent', 'attend', 'community', 'sporting', 'activities', 'physical', 'distancing', 'least', 'metres', 'maintained', 'accommodation', 'facilities', 'overnight', 'event', 'organisers', 'implement', 'covid', 'safety', 'carpooling', 'should', 'continue', 'avoidednew', 'newspaper']
['zealand', 'launches', 'covid', 'diary', 'appby', 'australian', 'associated', 'presspublished', 'updated', '2020belatedly', 'cases', 'covid', 'zealand', 'government', 'launched', 'covid', 'kiwis', 'track', 'their', 'movements', 'prime', 'minister', 'jacinda', 'ardern', 'launched', 'wednesday', 'labelling', 'digital', 'diary', 'differs', 'australian', 'version', 'linking', 'other', 'users', 'instead', 'allows', 'kiwis', 'check', 'different', 'venues', 'cafes', 'restaurants', 'avoid', 'onerous', 'writing', 'contact', 'details', 'every', 'place', 'visited', 'helps', 'users', 'about', 'their', 'movements', 'ardern', 'people', 'keeps', 'themselves', 'rather', 'adding', 'broader', 'repository', 'might', 'business', 'stage', 'government', 'authorities', 'though', 'functionality', 'added', 'update', 'month', 'different', 'functionality', 'means', 'there', 'threshold', 'required', 'useful', 'still', 'plenty', 'kiwis', 'taken', 'before', 'officially', 'launched', 'kiwis', 'almost', 'population', 'downloaded', 'wednesday', 'director', 'general', 'health', 'ashley', 'bloomfield', 'announced', 'there', 'cases', 'covid', 'zealand', 'fourth', 'there', 'cases', 'overall', 'fortnight', 'decreasing', 'numbers', 'nightclubs', 'first', 'since', 'march', 'thursday', 'provided', 'customers', 'seated', 'groups', 'under', 'receive', 'table', 'service', 'ardern', 'further', 'review', 'gathering', 'numbers', 'currently', 'except', 'funerals', 'which', 'monday', 'cabinet', 'meeting', 'regularly', 'adjust', 'settings', 'allow', 'activity', 'ardern', 'showing', 'virus', 'squashed', 'months', 'zealander', 'being', 'treated', 'hospital', 'covid', 'require', 'intensive', 'newspaper']
['records', 'covid', 'cases', 'deathsby', 'reuterspublished', 'updated', '2021london', 'reuters', 'britain', 'reported', 'daily', 'covid', 'cases', 'government', 'figures', 'showed', 'thursday', 'slightly', 'reported', 'before', 'which', 'highest', 'daily', 'total', 'since', 'february', 'britain', 'reported', 'seven', 'further', 'deaths', 'within', 'positive', 'covid', 'wednesday', 'taking', 'total', 'death', 'measure', 'british', 'adult', 'population', 'received', 'doses', 'covid', 'vaccine', 'received', 'least', 'reporting', 'david', 'milliken', 'editing', 'holton', 'newspaper']
['drugs', 'wrongly', 'touted', 'covid', 'treatmentsby', 'australian', 'associated', 'presspublished', 'august', 'updated', 'august', '2020medicines', 'falsely', 'touted', 'covid', 'treatments', 'hydroxychloroquine', 'treat', 'malaria', 'rheumatoid', 'arthritis', 'lupus', 'there', 'reliable', 'evidence', 'prevent', 'treat', 'covid', 'studies', 'found', 'things', 'worse', 'remdesivir', 'antiviral', 'medicine', 'people', 'hospital', 'severe', 'covid', 'recover', 'faster', 'people', 'milder', 'covid', 'dexamethasone', 'reduces', 'inflammation', 'treat', 'asthma', 'arthritis', 'reduces', 'dying', 'people', 'hospital', 'serious', 'covid', 'breathe', 'doesn', 'people', 'severe', 'covid', 'could', 'things', 'worse', 'ephedra', 'vitamin', 'other', 'complementary', 'medicines', 'found', 'effective', 'against', 'covid', 'outside', 'intensive', 'source', 'medicinewisenew', 'newspaper']
['covid', 'schools', 'study', 'breakdownby', 'australian', 'associated', 'presspublished', 'april', 'updated', 'april', '2020what', 'national', 'centre', 'immunisation', 'research', 'surveillance', 'ncirs', 'study', 'covid', 'schools', 'found', 'total', 'students', 'staff', 'members', 'schools', 'march', 'april', 'diagnosed', 'covid', 'people', 'students', 'staff', 'members', 'those', 'people', 'total', 'close', 'contacts', 'within', 'school', 'period', 'close', 'contacts', 'people', 'caught', 'covid', 'those', 'people', 'students', 'disease', 'others', 'students', 'passed', 'coronavirus', 'school', 'staff', 'members', 'spread', 'covid', 'within', 'schools', 'limited', 'virus', 'transmission', 'children', 'schools', 'influenza', 'newspaper']
['sydney', 'repeat', 'covid', 'breachesby', 'australian', 'associated', 'presspublished', 'october', 'updated', 'october', '2020the', 'sydney', 'portugal', 'community', 'being', 'forced', 'seven', 'after', 'failing', 'comply', 'covid', 'regulations', 'after', 'three', 'warnings', 'liquor', 'gaming', 'director', 'compliance', 'dimitri', 'argeres', 'portugal', 'marrickville', 'fourth', 'business', 'closed', 'covid', 'safety', 'breaches', 'close', 'wednesday', 'inspectors', 'visited', 'three', 'times', 'observed', 'breaches', 'relating', 'their', 'covid', 'safety', 'overbooking', 'groups', 'gaming', 'machine', 'spacing', 'inadequate', 'check', 'processes', 'argeres', 'tuesday', 'since', 'closure', 'order', 'issued', 'however', 'things', 'fined', 'longer', 'breaches', 'example', 'bookings', 'people', 'rather', 'while', 'guests', 'spaced', 'least', 'metres', 'apart', 'gaming', 'machines', 'changed', 'having', 'robust', 'digital', 'check', 'processes', 'allow', 'effective', 'contact', 'tracing', 'event', 'positive', 'visited', 'venue', 'argeres', 'service', 'covid', 'check', 'businesses', 'registered', 'covid', 'anyone', 'check', 'using', 'service', 'account', 'inspectors', 'liquor', 'gaming', 'trading', 'safework', 'conducted', 'covid', 'safety', 'visits', 'issued', 'penalties', 'worth', 'temporarily', 'closed', 'businesses', 'newspaper']
['tests', 'underway', 'covid', 'treatmentby', 'australian', 'associated', 'presspublished', 'august', 'updated', 'august', '2020australian', 'researchers', 'testing', 'treatment', 'coronavirus', 'provide', 'relief', 'those', 'infected', 'covid', 'limit', 'spread', 'professor', 'david', 'morris', 'international', 'adapted', 'cancer', 'treatment', 'known', 'bromac', 'could', 'prevent', 'virus', 'moving', 'lungs', 'carriers', 'spreading', 'other', 'people', 'trial', 'coronavirus', 'patients', 'being', 'established', 'melbourne', 'hospital', 'could', 'start', 'month', 'bromac', 'under', 'development', 'years', 'cancer', 'treatment', 'includes', 'components', 'together', 'dissolve', 'spike', 'covid', 'rendering', 'unable', 'infect', 'other', 'cells', 'agents', 'pineapple', 'enzyme', 'tested', 'after', 'observed', 'eating', 'pineapples', 'resistant', 'particular', 'gastro', 'conditions', 'taken', 'development', 'decade', 'asked', 'whether', 'adapted', 'treating', 'people', 'infected', 'covid', 'professor', 'david', 'morris', 'statement', 'monday', 'results', 'renders', 'covid', 'spike', 'ineffective', 'stopping', 'infecting', 'other', 'cells', 'results', 'treatment', 'confine', 'covid', 'throat', 'prevent', 'infection', 'infected', 'patients', 'passing', 'virus', 'hoped', 'infected', 'person', 'could', 'medication', 'first', 'signs', 'covid', 'virus', 'tracks', 'could', 'provide', 'effective', 'saving', 'lives', 'protecting', 'vulnerable', 'workers', 'preventing', 'hospitalisation', 'getting', 'people', 'infected', 'covid', 'returning', 'their', 'everyday', 'lives', 'researchers', 'stress', 'treatment', 'vaccine', 'covid', 'could', 'tandem', 'treatment', 'prevention', 'measure']

# LSA Model
number_of_topics=20
words=word_count_nscience
model_nscience=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science =dict(model_nscience.show_topic(0, topn=words))#dict and encoding matrix values

dict(model_nscience.show_topic(0,topn=5))

{'test': 0.5525012615032738,
 'peopl': 0.3271103290880657,
 'covid': 0.32350304714824835,
 'symptom': 0.20147297742697376,
 'travel': 0.15909033106510664}

from wordcloud import WordCloud
text = dict(model_nscience.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph7.png')

# LSA Model

a=pd.DataFrame()

for i in range(number_of_topics):
    words_covid_non_science =dict(model_nscience.show_topic(i, topn=words))
    b=pd.DataFrame(words_covid_non_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_nscience.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled7.png')

cosine-similarity
              test     peopl      covid    symptom     travel
test             1  0.336661  0.0349857   0.898669   0.716668
peopl     0.336661         1   0.308412   0.423709   0.271003
covid    0.0349857  0.308412          1 -0.0604921 -0.0986359
symptom   0.898669  0.423709 -0.0604921          1   0.755495
travel    0.716668  0.271003 -0.0986359   0.755495          1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='y',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="y")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig7.png')

covid-psychology.txt

document_list,titles,word_count_psy=load_data("","covid-psychology.txt",'NEW PAPER')

Number of words in text file : 86550
Total Number of Documents: 20

clean_text=preprocess_data(document_list)

['international', 'sociology2021', 'author', '2020article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissionsdoi', '0268580920948807journals', 'sagepub', 'isspsychology', 'politics', 'ofcovid', 'misinfodemics', 'peoplebelieve', 'misinfodemics', 'sonia', 'mukhtaruniversity', 'management', 'technology', 'lahore', 'pakistanabstractmisinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adversehealth', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicateevidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects', 'ofmisinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevierabout', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article', 'thisreview', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'articleshows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'apsychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive', 'thecrisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications', 'thedevelopment', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywordsconspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'sciencethe', 'mechanisms', 'misinfodemicsin', 'modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surroundingcorresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', 'com948807iss0010', '0268580920948807international', 'sociologymukhtarresearch', 'article2020article112', 'international', 'sociology', 'covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modernglobalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origindue', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'duringlockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'hasbecome', 'major', 'source', 'interaction', 'holman', 'covid', 'pandemic', 'outbreak', 'escalated', 'challenges', 'forhealthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerablemisinfodemics', 'mechanisms', 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'thematic', 'coding', 'categorizing', 'analyzing', 'qualitative', 'london', 'pages', 'french', 'gergle', 'oberlander', 'language', 'emotion', 'short', 'texts', 'proceedingsof', 'conference', 'computer', 'supported', 'cooperative', 'pages', 'goffman', 'stigma', 'notes', 'management', 'spoiled', 'identity', 'simon', 'schuster', 'gonzalez', 'hidalgo', 'barabasi', 'understanding', 'individual', 'human', 'mobility', 'patterns', 'nature', 'graham', 'haidt', 'nosek', 'liberals', 'conservatives', 'different', 'moral', 'foundations', 'journal', 'personality', 'social', 'psychology', 'graham', 'haidt', 'koleva', 'motyl', 'wojcik', 'ditto', 'moral', 'foundations', 'theory', 'thepragmatic', 'validity', 'moral', 'pluralism', 'advances', 'experimental', 'social', 'psychology', 'volume', 'pages', 'elsevier', 'grossman', 'rexer', 'thirumurthy', 'political', 'partisanship', 'influences', 'behavioral', 'responses', 'togovernors', 'recommendations', 'covid', 'prevention', 'united', 'states', 'available', '3578695', 'hebert', 'clemow', 'pbert', 'ockene', 'ockene', 'social', 'desirability', 'dietary', 'report', 'maycompromise', 'validity', 'dietary', 'intake', 'measures', 'international', 'journal', 'epidemiology', 'hochreiter', 'schmidhuber', 'short', 'memory', 'neural', 'computation', 'hofstede', 'hofstede', 'minkov', 'cultures', 'organizations', 'software', 'volume', 'mcgraw', 'holmes', 'connor', 'perry', 'tracey', 'wessely', 'arseneault', 'ballard', 'christensen', 'silver', 'everall', 'multidisciplinary', 'research', 'priorities', 'covid', 'pandemic', 'action', 'mentalhealth', 'science', 'lancet', 'psychiatry', 'jiang', 'assessment', 'public', 'attention', 'perception', 'emotional', 'andbehavioural', 'responses', 'covid', 'outbreak', 'social', 'media', 'surveillance', 'china', 'perception', 'emotionaland', 'behavioural', 'responses', 'covid', 'outbreak', 'social', 'media', 'surveillance', 'china', 'huang', 'bidirectional', 'models', 'sequence', 'tagging', 'arxiv', 'preprint', 'arxiv', '01991', 'auriemma', 'cashaw', 'report', 'underreporting', 'depression', 'journal', 'ofpersonality', 'assessment', 'epidemic', 'psychology', 'works', 'twitter', 'husnayain', 'applications', 'google', 'search', 'trends', 'communication', 'infectiousdisease', 'management', 'study', 'covid', 'outbreak', 'taiwan', 'international', 'journal', 'infectious', 'diseases', 'jimeno', 'yepes', 'mackinlay', 'identifying', 'diseases', 'drugs', 'symptoms', 'twitter', 'studies', 'health', 'technology', 'informatics', 'johnson', 'fendrich', 'modeling', 'sources', 'report', 'survey', 'epidemiology', 'annals', 'ofepidemiology', 'tobin', 'massey', 'anderson', 'measuring', 'emotional', 'expression', 'linguisticinquiry', 'count', 'american', 'journal', 'psychology', 'pages', 'huremovi', 'psychology', 'pandemic', 'psychiatry', 'pandemics', 'pages', 'springer', 'kornfield', 'gustafson', 'before', 'relapse', 'howlanguage', 'online', 'discussion', 'forum', 'predicts', 'risky', 'drinking', 'among', 'those', 'recovery', 'healthcommunication', 'kouzy', 'jaoude', 'kraitem', 'karam', 'zarka', 'traboulsi', 'baddour', 'coronavirus', 'viral', 'quantifying', 'covid', 'misinformation', 'epidemic', 'twitter', 'cureus', 'ubler', 'wessler', 'avioli', 'death', 'dying', 'goodchild', 'spatial', 'temporal', 'socioeconomic', 'patterns', 'twitter', 'flickr', 'cartography', 'geographic', 'information', 'science', 'impact', 'covid', 'epidemic', 'declaration', 'psychologicalconsequences', 'study', 'active', 'weibo', 'users', 'international', 'journal', 'environmental', 'research', 'public', 'health', 'liang', 'effect', 'covid', 'youth', 'mental', 'health', 'psychiatric', 'quarterly', 'pages', 'goyal', 'joshi', 'lewis', 'zettlemoyer', 'stoyanov', 'roberta', 'robustly', 'optimized', 'pretraining', 'approach', 'arxiv', 'preprint', 'arxiv', '11692', 'utkepohl', 'introduction', 'multiple', 'series', 'analysis', 'springer', 'science', 'business', 'media', 'maslow', 'theory', 'human', 'motivation', 'psychological', 'review', 'mcconnell', 'banks', 'avian', 'planning', 'possible', 'pandemic', 'trading', 'technology', 'mohammad', 'turney', 'crowdsourcing', 'emotion', 'association', 'lexicon', 'computational', 'intelligence', 'moinet', 'pastor', 'satorras', 'barrat', 'effect', 'perception', 'epidemic', 'spreading', 'temporal', 'networks', 'physical', 'review', '012313', 'connor', 'balasubramanyan', 'routledge', 'smith', 'tweets', 'polls', 'linking', 'sentimentto', 'public', 'opinion', 'series', 'fourth', 'international', 'conference', 'weblogs', 'social', 'media', 'oyeyemi', 'gabarron', 'ebola', 'twitter', 'misinformation', 'dangerous', 'combination', 'g6178', 'painter', 'political', 'beliefs', 'affect', 'compliance', 'covid', 'social', 'distancing', 'orders', 'available', 'ssrn3569098', 'palshikar', 'simple', 'algorithms', 'detection', 'series', 'advanced', 'dataanalysis', 'business', 'analytics', 'intelligence', 'volume', 'paszke', 'gross', 'chintala', 'chanan', 'devito', 'desmaison', 'antiga', 'lerer', 'automatic', 'differentiation', 'pytorch', 'proceedings', 'advances', 'neural', 'information', 'processing', 'systemsautodiff', 'workshop', 'epidemic', 'psychology', 'works', 'twitterj', 'pennington', 'socher', 'manning', 'glove', 'global', 'vectors', 'representation', 'proceedings', 'theconference', 'empirical', 'methods', 'natural', 'language', 'processing', 'pages', 'association', 'computationallinguistics', 'perrin', 'anderson', 'share', 'adults', 'using', 'social', 'media', 'including', 'facebook', 'ismostly', 'unchanged', 'since', 'https', 'pewresearch', 'share', 'adults', 'using', 'social', 'media', 'including', 'facebook', 'mostly', 'unchanged', 'since', 'plutchik', 'emotions', 'university', 'press', 'america', 'pulido', 'villarejo', 'carballido', 'redondo', 'covid', 'infodemic', 'retweetsfor', 'science', 'based', 'information', 'coronavirus', 'false', 'information', 'international', 'sociology', 'page0268580920914755', 'nationwide', 'survey', 'psychological', 'distress', 'among', 'chinesepeople', 'covid', 'epidemic', 'implications', 'policy', 'recommendations', 'general', 'psychiatry', 'rezapour', 'diesner', 'enhancing', 'measurement', 'social', 'effects', 'capturing', 'morality', 'inproceedings', 'tenth', 'workshop', 'computational', 'approaches', 'subjectivity', 'sentiment', 'social', 'mediaanalysis', 'pages', 'russell', 'mining', 'social', 'mining', 'facebook', 'twitter', 'linkedin', 'google', 'github', 'reilly', 'media', 'alani', 'alleviating', 'sparsity', 'twitter', 'sentiment', 'analysis', 'workshop', 'proceedings', 'salathe', 'bengtsson', 'bodnar', 'brewer', 'brownstein', 'buckee', 'campbell', 'cattuto', 'khandelwal', 'mabry', 'digital', 'epidemiology', 'comput', 'e1002616', 'scepanovic', 'martin', 'lopez', 'quercia', 'baykaner', 'extracting', 'medical', 'entities', 'social', 'media', 'inproceedings', 'conference', 'health', 'inference', 'learning', 'pages', 'schutz', 'luckmann', 'zaner', 'engelhardt', 'structures', 'world', 'number', 'northwest', 'university', 'studies', 'phenomenology', 'existential', 'philosophy', 'northwestern', 'university', 'press', 'isbn9780810106222', 'https', 'books', 'google', 'books', 'lgxbxi0xsh8c', 'gustafson', 'hawkins', 'cleary', 'mctavish', 'pingree', 'eliason', 'lumpkins', 'effects', 'prayer', 'religious', 'expression', 'within', 'computer', 'support', 'groups', 'women', 'breastcancer', 'psycho', 'oncology', 'journal', 'psychological', 'social', 'behavioral', 'dimensions', 'cancer', 'rudzicz', 'detecting', 'anxiety', 'through', 'reddit', 'proceedings', 'fourth', 'workshop', 'compu', 'tational', 'linguistics', 'clinical', 'psychology', 'linguistic', 'signal', 'clinical', 'reality', 'pages', 'shultz', 'baingana', 'neria', 'ebola', 'outbreak', 'mental', 'health', 'current', 'status', 'recom', 'mended', 'response', 'simms', 'ramstedt', 'richards', 'martinez', 'giraud', 'carrier', 'detecting', 'cognitive', 'distortionsthrough', 'machine', 'learning', 'analytics', 'international', 'conference', 'healthcare', 'informatics', 'pages', 'smith', 'shinebourne', 'interpretative', 'phenomenological', 'analysis', 'american', 'psychological', 'association', 'sommariva', 'vamos', 'mantzarlis', 'martinez', 'tyson', 'spreading', 'exploringhealth', 'messages', 'social', 'media', 'implications', 'health', 'professionals', 'using', 'study', 'american', 'journalof', 'health', 'education', 'strong', 'epidemic', 'psychology', 'model', 'sociology', 'health', 'illness', 'tajfel', 'turner', 'austin', 'worchel', 'integrative', 'theory', 'intergroup', 'conflict', 'organizationalidentity', 'tausczik', 'pennebaker', 'psychological', 'meaning', 'words', 'computerized', 'analysismethods', 'journal', 'language', 'social', 'psychology', 'epidemic', 'psychology', 'works', 'twitter', '21the', 'economist', 'millions', 'stuck', 'online', 'wellness', 'dustry', 'booming', 'https', 'economist', 'international', 'millions', 'stuck', 'online', 'wellness', 'industry', 'booming', 'bavel', 'baicker', 'boggio', 'capraro', 'cichocka', 'cikara', 'crockett', 'douglas', 'druckman', 'using', 'social', 'behavioural', 'science', 'support', 'covid', 'pandemic', 'response', 'nature', 'human', 'behaviour', 'pages', 'varol', 'ferrara', 'davis', 'menczer', 'flammini', 'online', 'human', 'interactions', 'detection', 'estimation', 'characterization', 'eleventh', 'international', 'conference', 'social', 'media', 'immediate', 'psychological', 'responses', 'associatedfactors', 'during', 'initial', 'stage', 'coronavirus', 'disease', 'covid', 'epidemic', 'among', 'general', 'populationin', 'china', 'international', 'journal', 'environmental', 'research', 'public', 'health', 'waterloo', 'baumgartner', 'peter', 'valkenburg', 'norms', 'online', 'expressions', 'emotion', 'comparing', 'facebook', 'twitter', 'instagram', 'whatsapp', 'media', 'society', 'windsor', 'dowell', 'graesser', 'language', 'autocrats', 'leaders', 'language', 'natural', 'disaster', 'crises', 'hazards', 'crisis', 'public', 'policy', 'wojcik', 'hughes', 'sizing', 'twitter', 'users', 'washington', 'research', 'center', 'theis', 'kordy', 'language', 'eating', 'disorder', 'blogs', 'psychological', 'implications', 'social', 'onlineactivity', 'journal', 'language', 'social', 'psychology', 'propagating', 'debunking', 'conspiracy', 'theories', 'twitter', 'during', 'virus', 'outbreak', 'cyberpsychology', 'behavior', 'social', 'networking', 'torres', 'menczer', 'prevalence', 'credibility', 'information', 'twitter', 'during', 'thecovid', 'outbreak', 'arxiv', 'preprint', 'arxiv', '14484', 'acknowledgmentswe', 'thank', 'sarah', 'konrath', 'rosta', 'farzan', 'licia', 'capra', 'their', 'useful', 'feedback', 'manuscript', 'researchwas', 'partly', 'supported', 'grant', 'green', 'routes', '869764', 'availabilitythe', 'daily', 'aggregates', 'measurements', 'available', 'https', 'figshare', '14892642', 'tweet', 'available', 'https', 'github', 'echen102', 'covid', 'tweetids', 'other', 'datasetsand', 'visualizations', 'available', 'project', 'social', 'dynamics', 'epidemicpsychology', 'competing', 'intereststhe', 'author', 'declare', 'competing', 'interests', 'epidemic', 'psychology', 'works', 'twittersupplementary', 'materialsspatial', 'representativeness', 'twitter', 'geographical', 'penetration', 'twitterdata', 'reflects', 'spatial', 'distribution', 'population', 'compared', 'activity', 'state', 'withthe', 'census', 'population', 'figure', 'shows', 'number', 'twitter', 'users', 'volume', 'their', 'tweetscorrelate', 'strongly', 'census', 'population', 'population', 'estimates', 'level', 'states', 'https', 'census', '17log', 'population', '1213141516log', 'tweets', '08814', '17log', 'population', '91011121314log', 'users', '106figure', 'correlation', 'census', 'population', 'states', 'estimated', 'thenumber', 'tweets', 'posted', 'those', 'states', 'number', 'unique', 'twitter', 'users', 'postedat', 'least', 'tweet', 'right', 'during', 'period', 'analysis', 'linear', 'shown', 'together', 'thecoefficient', 'determination', 'linear', 'regression', 'pearson', 'correlation', 'coefficient', 'andthe', 'coefficient', 'number', 'tweets', 'twitter', 'users', 'scale', 'almost', 'linearlywith', 'population', 'estimates', 'signals', 'racism', 'twitter', 'discussions', 'corroborate', 'qualitative', 'intuition', 'first', 'phase', 'wascharacterized', 'discussions', 'depicted', 'pandemic', 'foreign', 'problem', 'often', 'using', 'racist', 'undertones', 'wemeasured', 'volume', 'tweets', 'containing', 'hashtags', 'clear', 'indicators', 'content', 'either', 'racist', 'oraimed', 'antagonizing', 'china', 'manually', 'selected', 'those', 'hashtags', 'hashtags', 'appeared', 'atleast', 'times', 'dataset', 'figure', 'shows', 'normalized', 'volume', 'those', 'hashtags', 'theypeak', 'during', 'first', 'phase', 'related', 'mentions', 'focus', 'twitter', 'discussions', 'china', 'asianforeigners', 'their', 'peaks', 'breakdown', 'phases', 'state', 'during', 'period', 'analysis', 'country', 'fragmented', 'regions', 'thatwere', 'dealing', 'different', 'stages', 'outbreak', 'policies', 'contrast', 'varied', 'across', 'states', 'assesswhether', 'those', 'differences', 'impacted', 'unfolding', 'psycho', 'social', 'epidemics', 'across', 'states', 'computed', 'themeasures', 'represent', 'relative', 'presence', 'three', 'phases', 'refusal', 'anger', 'acceptance', 'state', 'individually', 'results', 'shown', 'figure', 'despite', 'minor', 'differences', 'state', 'level', 'curvesindicate', 'three', 'phases', 'place', 'states', 'sequence', 'phases', 'consistent', 'across', 'states', 'refusal', 'followed', 'anger', 'which', 'followed', 'acceptance', 'there', 'little', 'variation', 'thetimes', 'transition', 'between', 'phases', 'epidemic', 'psychology', 'works', 'twitter']

# LSA Model
number_of_topics=20
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))#dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_psy_articles=create_gensim_lsa_model(clean_text,number_of_topics,words)

dict(model_psy.show_topic(0,topn=5))

{'covid': 0.3733564956224112,
 'psycholog': 0.2678599109041967,
 'social': 0.251779781716548,
 'epidem': 0.19223792506780354,
 'health': 0.18194229083665667}

from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph27.png')

# LSA Model
a=pd.DataFrame()

for i in range(number_of_topics):
    words_psychology =dict(model_psy.show_topic(i, topn=words))
    b=pd.DataFrame(words_psychology,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled8.png')

cosine-similarity
               covid psycholog     social     epidem     health
covid              1  0.150447  0.0375207 -0.0516623  0.0207802
psycholog   0.150447         1   0.168003   0.125162   0.380477
social     0.0375207  0.168003          1    0.44382  0.0839178
epidem    -0.0516623  0.125162    0.44382          1  0.0126691
health     0.0207802  0.380477  0.0839178  0.0126691          1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig8.png')

def mantel_test(n):
    words_science = dict(model_science.show_topic(0, topn=word_count_science))
    words_psy = dict(model_psy.show_topic(0, topn=word_count_psy))

    top_words_science = dict(model_science.show_topic(0, topn=n)) 
    top_words_psy = dict(model_psy.show_topic(0,topn=n)) 


    c={} #law words in non law
    for key in top_words_science:
        try:
            c[key] = words_psy[key]
        except KeyError:
            c[key] = 0

    top_words_psy.update(c)

    b={} #non law words in law
    for key in top_words_psy:
        try:
            b[key] = words_science[key]
        except KeyError:
            b[key] = 0

    b.update(top_words_science)
    top_words_science=b

    #pairwise distance for law
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_science =dict(model_science.show_topic(i, topn=word_count_science))
        b=pd.DataFrame(words_from_science,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_science.keys()), 
                      index = list(top_words_science.keys()))

    l = list(top_words_science.keys())

    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df1 = df.to_numpy()
    #pairwise distace for non-law
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_psy =dict(model_psy.show_topic(i, topn=word_count_psy))
        b=pd.DataFrame(words_from_psy,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_psy.keys()), 
                      index = list(top_words_psy.keys()))

    l = list(top_words_psy.keys())
    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df2 = df.to_numpy()
    

    dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])

    

    dist2 = list(df2[np.triu_indices(len(top_words_psy), k = 1)])

    return mantel.test(dist1, dist2, perms=100000, method='pearson', tail='upper')

mantel_test(5)

Pairwise-distance
                 covid psycholog    social       epidem    health      imag  \
covid      1.49012e-08  0.707357  0.809565     0.729092  0.692671   1.01386   
psycholog     0.707357         0  0.433075     0.118413  0.179014  0.812021   
social        0.809565  0.433075         0     0.453616  0.435767  0.956542   
epidem        0.729092  0.118413  0.453616  1.86265e-09  0.203294  0.819662   
health        0.692671  0.179014  0.435767     0.203294         0  0.846649   
imag           1.01386  0.812021  0.956542     0.819662  0.846649         0   
model         0.966408  0.710159  0.835567     0.688397  0.715407   1.13159   
dataset       0.983197  0.633964  0.732054     0.664799  0.660846  0.935924   
class         0.747855  0.506564  0.696674     0.535392  0.541672   0.94137   

              model   dataset     class  
covid      0.966408  0.983197  0.747855  
psycholog  0.710159  0.633964  0.506564  
social     0.835567  0.732054  0.696674  
epidem     0.688397  0.664799  0.535392  
health     0.715407  0.660846  0.541672  
imag        1.13159  0.935924   0.94137  
model             0  0.950057  0.851355  
dataset    0.950057         0  0.788631  
class      0.851355  0.788631         0  
Pairwise-distance
                 covid psycholog       social    epidem    health  \
covid      1.05367e-08  0.737681     0.778668  0.779457  0.898325   
psycholog     0.737681         0      0.65021  0.633251  0.666982   
social        0.778668   0.65021  1.05367e-08  0.500852  0.798641   
epidem        0.779457  0.633251     0.500852         0  0.797412   
health        0.898325  0.666982     0.798641  0.797412         0   
imag          0.608882  0.518059     0.485242  0.452257  0.667781   
model         0.601493  0.544249      0.45706  0.384569  0.700157   
dataset       0.616357  0.509412     0.496815  0.440816  0.665726   
class         0.621675  0.509301     0.502068  0.441585  0.663889   

                  imag     model    dataset      class  
covid         0.608882  0.601493   0.616357   0.621675  
psycholog     0.518059  0.544249   0.509412   0.509301  
social        0.485242   0.45706   0.496815   0.502068  
epidem        0.452257  0.384569   0.440816   0.441585  
health        0.667781  0.700157   0.665726   0.663889  
imag       6.58545e-10  0.188527  0.0437515  0.0566211  
model         0.188527         0   0.167483   0.181562  
dataset      0.0437515  0.167483          0  0.0311504  
class        0.0566211  0.181562  0.0311504          0  

(-0.4509779617153421, 0.9238, -1.4121586036305867)

mantel_test(10)

Pairwise-distance
                 covid  psycholog    social       epidem    health    pandem  \
covid      1.49012e-08   0.707357  0.809565     0.729092  0.692671  0.721742   
psycholog     0.707357          0  0.433075     0.118413  0.179014  0.201225   
social        0.809565   0.433075         0     0.453616  0.435767  0.405726   
epidem        0.729092   0.118413  0.453616  1.86265e-09  0.203294  0.147007   
health        0.692671   0.179014  0.435767     0.203294         0  0.233572   
pandem        0.721742   0.201225  0.405726     0.147007  0.233572         0   
group          0.68862  0.0830849   0.42423     0.151352  0.181242   0.21846   
peopl         0.746179   0.298629  0.396129     0.327835  0.345893  0.329006   
studi         0.660518   0.245178  0.432976     0.307804  0.268351  0.325444   
categori      0.712445  0.0595527  0.437718     0.143522  0.193169   0.22002   
imag           1.01386   0.812021  0.956542     0.819662  0.846649  0.849144   
model         0.966408   0.710159  0.835567     0.688397  0.715407  0.707811   
dataset       0.983197   0.633964  0.732054     0.664799  0.660846  0.675803   
class         0.747855   0.506564  0.696674     0.535392  0.541672  0.579556   
learn         0.853802   0.565199  0.741625      0.56658  0.566154  0.575911   
perform       0.731261   0.366316  0.596691     0.408269  0.422608   0.42702   
train         0.687667   0.343316  0.560939     0.347012  0.387858  0.352473   
detect        0.744875   0.368773  0.603099     0.401454  0.452272  0.466653   
use           0.638247   0.234483  0.514289     0.265218  0.280529  0.329639   

               group        peopl        studi   categori      imag     model  \
covid        0.68862     0.746179     0.660518   0.712445   1.01386  0.966408   
psycholog  0.0830849     0.298629     0.245178  0.0595527  0.812021  0.710159   
social       0.42423     0.396129     0.432976   0.437718  0.956542  0.835567   
epidem      0.151352     0.327835     0.307804   0.143522  0.819662  0.688397   
health      0.181242     0.345893     0.268351   0.193169  0.846649  0.715407   
pandem       0.21846     0.329006     0.325444    0.22002  0.849144  0.707811   
group              0     0.308936     0.240891  0.0981544  0.806927  0.732497   
peopl       0.308936  5.26836e-09     0.387064   0.309772  0.892902  0.779141   
studi       0.240891     0.387064  5.26836e-09   0.236468  0.842811   0.74784   
categori   0.0981544     0.309772     0.236468          0  0.817282  0.699807   
imag        0.806927     0.892902     0.842811   0.817282         0   1.13159   
model       0.732497     0.779141      0.74784   0.699807   1.13159         0   
dataset     0.660014      0.70194     0.609506   0.630512  0.935924  0.950057   
class       0.542816     0.601803     0.579555   0.515653   0.94137  0.851355   
learn       0.559582     0.666994     0.574178   0.555292   0.99937  0.890738   
perform      0.37624      0.45761      0.41158   0.340646  0.875433  0.595767   
train       0.356952     0.451924      0.46984   0.348798  0.733119  0.723461   
detect      0.377182     0.476563     0.473258    0.35669  0.836253  0.721344   
use         0.203453     0.354012     0.320037   0.253145  0.829421  0.749587   

            dataset     class     learn      perform     train    detect  \
covid      0.983197  0.747855  0.853802     0.731261  0.687667  0.744875   
psycholog  0.633964  0.506564  0.565199     0.366316  0.343316  0.368773   
social     0.732054  0.696674  0.741625     0.596691  0.560939  0.603099   
epidem     0.664799  0.535392   0.56658     0.408269  0.347012  0.401454   
health     0.660846  0.541672  0.566154     0.422608  0.387858  0.452272   
pandem     0.675803  0.579556  0.575911      0.42702  0.352473  0.466653   
group      0.660014  0.542816  0.559582      0.37624  0.356952  0.377182   
peopl       0.70194  0.601803  0.666994      0.45761  0.451924  0.476563   
studi      0.609506  0.579555  0.574178      0.41158   0.46984  0.473258   
categori   0.630512  0.515653  0.555292     0.340646  0.348798   0.35669   
imag       0.935924   0.94137   0.99937     0.875433  0.733119  0.836253   
model      0.950057  0.851355  0.890738     0.595767  0.723461  0.721344   
dataset           0  0.788631  0.882097     0.729462  0.754527   0.75098   
class      0.788631         0  0.726501     0.654866  0.591455  0.605089   
learn      0.882097  0.726501         0     0.629472  0.603452  0.659699   
perform    0.729462  0.654866  0.629472  7.45058e-09  0.442139  0.472107   
train      0.754527  0.591455  0.603452     0.442139         0  0.446377   
detect      0.75098  0.605089  0.659699     0.472107  0.446377         0   
use        0.656879  0.547929  0.578637      0.39981  0.438878  0.418005   

                   use  
covid         0.638247  
psycholog     0.234483  
social        0.514289  
epidem        0.265218  
health        0.280529  
pandem        0.329639  
group         0.203453  
peopl         0.354012  
studi         0.320037  
categori      0.253145  
imag          0.829421  
model         0.749587  
dataset       0.656879  
class         0.547929  
learn         0.578637  
perform        0.39981  
train         0.438878  
detect        0.418005  
use        3.72529e-09  
Pairwise-distance
                 covid psycholog       social    epidem    health    pandem  \
covid      1.05367e-08  0.737681     0.778668  0.779457  0.898325  0.586377   
psycholog     0.737681         0      0.65021  0.633251  0.666982   0.55745   
social        0.778668   0.65021  1.05367e-08  0.500852  0.798641  0.639384   
epidem        0.779457  0.633251     0.500852         0  0.797412  0.640981   
health        0.898325  0.666982     0.798641  0.797412         0  0.657499   
pandem        0.586377   0.55745     0.639384  0.640981  0.657499         0   
group         0.977762  0.939908      0.84304  0.934338   1.03874  0.902724   
peopl         0.786247  0.712449     0.571794  0.723179    0.8931  0.610391   
studi         0.521524  0.540289     0.566199  0.442122  0.765614  0.519571   
categori      0.708764  0.555281     0.470567   0.25915  0.714492  0.504374   
imag          0.608882  0.518059     0.485242  0.452257  0.667781  0.410422   
model         0.601493  0.544249      0.45706  0.384569  0.700157  0.448981   
dataset       0.616357  0.509412     0.496815  0.440816  0.665726  0.417791   
class         0.621675  0.509301     0.502068  0.441585  0.663889  0.425047   
learn         0.614328  0.524196     0.504745   0.43775  0.676962  0.397198   
perform       0.652719  0.545755     0.543241  0.489701  0.709983  0.448316   
train         0.613403   0.51447     0.535237  0.464751  0.678239  0.416247   
detect        0.615537  0.509455     0.489411  0.420328  0.670271  0.417696   
use            0.62976  0.482207     0.471984  0.437462  0.705997  0.420307   

              group     peopl        studi     categori         imag  \
covid      0.977762  0.786247     0.521524     0.708764     0.608882   
psycholog  0.939908  0.712449     0.540289     0.555281     0.518059   
social      0.84304  0.571794     0.566199     0.470567     0.485242   
epidem     0.934338  0.723179     0.442122      0.25915     0.452257   
health      1.03874    0.8931     0.765614     0.714492     0.667781   
pandem     0.902724  0.610391     0.519571     0.504374     0.410422   
group             0  0.822353     0.801354     0.837159     0.788985   
peopl      0.822353         0       0.6697     0.627939     0.521513   
studi      0.801354    0.6697  7.45058e-09     0.406992     0.327475   
categori   0.837159  0.627939     0.406992  5.26836e-09     0.292367   
imag       0.788985  0.521513     0.327475     0.292367  6.58545e-10   
model      0.817135  0.570108     0.362216      0.21854     0.188527   
dataset    0.782328  0.526123     0.329123      0.27569    0.0437515   
class       0.77953  0.516103     0.332107     0.282013    0.0566211   
learn      0.790215  0.536941     0.338135     0.270631     0.116594   
perform    0.813742  0.574154     0.401387     0.344149     0.209862   
train      0.805884  0.542088     0.361415     0.304526     0.142135   
detect     0.784408  0.534872     0.328239     0.253161    0.0594493   
use          0.8103  0.542373      0.36093     0.286119     0.163526   

              model    dataset      class      learn   perform        train  \
covid      0.601493   0.616357   0.621675   0.614328  0.652719     0.613403   
psycholog  0.544249   0.509412   0.509301   0.524196  0.545755      0.51447   
social      0.45706   0.496815   0.502068   0.504745  0.543241     0.535237   
epidem     0.384569   0.440816   0.441585    0.43775  0.489701     0.464751   
health     0.700157   0.665726   0.663889   0.676962  0.709983     0.678239   
pandem     0.448981   0.417791   0.425047   0.397198  0.448316     0.416247   
group      0.817135   0.782328    0.77953   0.790215  0.813742     0.805884   
peopl      0.570108   0.526123   0.516103   0.536941  0.574154     0.542088   
studi      0.362216   0.329123   0.332107   0.338135  0.401387     0.361415   
categori    0.21854    0.27569   0.282013   0.270631  0.344149     0.304526   
imag       0.188527  0.0437515  0.0566211   0.116594  0.209862     0.142135   
model             0   0.167483   0.181562   0.188963  0.271471     0.223534   
dataset    0.167483          0  0.0311504   0.102298  0.206237     0.129523   
class      0.181562  0.0311504          0   0.104821  0.211188     0.135991   
learn      0.188963   0.102298   0.104821          0  0.225444     0.108693   
perform    0.271471   0.206237   0.211188   0.225444         0      0.17061   
train      0.223534   0.129523   0.135991   0.108693   0.17061  2.63418e-09   
detect     0.154397  0.0315202  0.0439117  0.0909753  0.209923     0.128111   
use        0.153048   0.147371   0.156009   0.173902  0.255026     0.200387   

              detect       use  
covid       0.615537   0.62976  
psycholog   0.509455  0.482207  
social      0.489411  0.471984  
epidem      0.420328  0.437462  
health      0.670271  0.705997  
pandem      0.417696  0.420307  
group       0.784408    0.8103  
peopl       0.534872  0.542373  
studi       0.328239   0.36093  
categori    0.253161  0.286119  
imag       0.0594493  0.163526  
model       0.154397  0.153048  
dataset    0.0315202  0.147371  
class      0.0439117  0.156009  
learn      0.0909753  0.173902  
perform     0.209923  0.255026  
train       0.128111  0.200387  
detect             0  0.142704  
use         0.142704         0  

(-0.37001348458330224, 0.96178, -1.6840345787126263)

def mantel_test_n(n):
    words_science = dict(model_science.show_topic(0, topn=word_count_science))
    words_nscience = dict(model_nscience.show_topic(0, topn=word_count_nscience))

    top_words_science = dict(model_science.show_topic(0, topn=n)) 
    top_words_nscience = dict(model_nscience.show_topic(0,topn=n)) 


    c={} #law words in non law
    for key in top_words_science:
        try:
            c[key] = words_nscience[key]
        except KeyError:
            c[key] = 0

    top_words_nscience.update(c)

    b={} 
    for key in top_words_nscience:
        try:
            b[key] = words_science[key]
        except KeyError:
            b[key] = 0

    b.update(top_words_science)
    top_words_science=b

    #pairwise distance 
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_science =dict(model_science.show_topic(i, topn=word_count_science))
        b=pd.DataFrame(words_from_science,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_science.keys()), 
                      index = list(top_words_science.keys()))

    l = list(top_words_science.keys())

    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df1 = df.to_numpy()
    #pairwise distace for non-law
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_nscience =dict(model_nscience.show_topic(i, topn=word_count_nscience))
        b=pd.DataFrame(words_from_nscience,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_nscience.keys()), 
                      index = list(top_words_nscience.keys()))

    l = list(top_words_nscience.keys())
    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df2 = df.to_numpy()
    

    dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])

    

    dist2 = list(df2[np.triu_indices(len(top_words_nscience), k = 1)])

    return mantel.test(dist1, dist2, perms=100000, method='pearson', tail='upper')

mantel_test_n(5)

Pairwise-distance
                test        peopl        covid    symptom     travel  \
test     1.05367e-08     0.434425      0.77812   0.448064   0.443627   
peopl       0.434425  5.26836e-09     0.746179   0.308778   0.299986   
covid        0.77812     0.746179  1.49012e-08   0.698228   0.705141   
symptom     0.448064     0.308778     0.698228          0  0.0703243   
travel      0.443627     0.299986     0.705141  0.0703243          0   
imag        0.941967     0.892902      1.01386   0.814187   0.814211   
model       0.839523     0.779141     0.966408   0.715226   0.708682   
dataset     0.762406      0.70194     0.983197   0.628236   0.633547   
class       0.649276     0.601803     0.747855   0.525632   0.508831   

             imag     model   dataset     class  
test     0.941967  0.839523  0.762406  0.649276  
peopl    0.892902  0.779141   0.70194  0.601803  
covid     1.01386  0.966408  0.983197  0.747855  
symptom  0.814187  0.715226  0.628236  0.525632  
travel   0.814211  0.708682  0.633547  0.508831  
imag            0   1.13159  0.935924   0.94137  
model     1.13159         0  0.950057  0.851355  
dataset  0.935924  0.950057         0  0.788631  
class     0.94137  0.851355  0.788631         0  
Pairwise-distance
                test     peopl     covid   symptom    travel imag  \
test     1.05367e-08   0.71754   1.00834  0.456049  0.537165    0   
peopl        0.71754         0  0.790457  0.495751  0.533887    0   
covid        1.00834  0.790457         0  0.826913  0.825439    0   
symptom     0.456049  0.495751  0.826913         0  0.182534    0   
travel      0.537165  0.533887  0.825439  0.182534         0    0   
imag               0         0         0         0         0    0   
model       0.688797  0.546779  0.765173  0.273475   0.24722    0   
dataset            0         0         0         0         0    0   
class       0.688693  0.529488  0.768325  0.277696  0.252525    0   

               model dataset        class  
test        0.688797       0     0.688693  
peopl       0.546779       0     0.529488  
covid       0.765173       0     0.768325  
symptom     0.273475       0     0.277696  
travel       0.24722       0     0.252525  
imag               0       0            0  
model    6.58545e-10       0    0.0560273  
dataset            0       0            0  
class      0.0560273       0  6.58545e-10  

(-0.3075503588914985, 0.8241, -0.9982393129609141)

mantel_test_n(10)

Pairwise-distance
                    test        peopl        covid    symptom     travel  \
test         1.05367e-08     0.434425      0.77812   0.448064   0.443627   
peopl           0.434425  5.26836e-09     0.746179   0.308778   0.299986   
covid            0.77812     0.746179  1.49012e-08   0.698228   0.705141   
symptom         0.448064     0.308778     0.698228          0  0.0703243   
travel          0.443627     0.299986     0.705141  0.0703243          0   
fever           0.443251     0.305331     0.696873  0.0517681   0.042983   
infect          0.586153     0.539245     0.810014   0.449323   0.443879   
contact         0.400548      0.27183     0.705116  0.0999925   0.105931   
respiratori     0.445605      0.30598      0.70242  0.0629804  0.0517137   
worker          0.410683     0.286012     0.707893  0.0754608  0.0392005   
imag            0.941967     0.892902      1.01386   0.814187   0.814211   
model           0.839523     0.779141     0.966408   0.715226   0.708682   
dataset         0.762406      0.70194     0.983197   0.628236   0.633547   
class           0.649276     0.601803     0.747855   0.525632   0.508831   
learn           0.746572     0.666994     0.853802   0.578567   0.557406   
perform         0.588301      0.45761     0.731261   0.360208   0.366167   
train           0.585634     0.451924     0.687667   0.355796   0.340721   
detect          0.567781     0.476563     0.744875   0.387151   0.369656   
use             0.500907     0.354012     0.638247   0.229288   0.234633   

                 fever    infect    contact respiratori     worker      imag  \
test          0.443251  0.586153   0.400548    0.445605   0.410683  0.941967   
peopl         0.305331  0.539245    0.27183     0.30598   0.286012  0.892902   
covid         0.696873  0.810014   0.705116     0.70242   0.707893   1.01386   
symptom      0.0517681  0.449323  0.0999925   0.0629804  0.0754608  0.814187   
travel        0.042983  0.443879   0.105931   0.0517137  0.0392005  0.814211   
fever                0  0.441408   0.101504   0.0465915  0.0524024  0.816775   
infect        0.441408         0   0.453241    0.452121   0.440275  0.946344   
contact       0.101504  0.453241          0    0.105907  0.0881997  0.828223   
respiratori  0.0465915  0.452121   0.105907           0  0.0595578   0.80648   
worker       0.0524024  0.440275  0.0881997   0.0595578          0  0.815193   
imag          0.816775  0.946344   0.828223     0.80648   0.815193         0   
model         0.719424   0.77787    0.72059    0.705195   0.711361   1.13159   
dataset       0.639605  0.849464   0.644935     0.64003   0.630681  0.935924   
class         0.515307  0.711211     0.5262    0.509852   0.509444   0.94137   
learn         0.566059  0.635198   0.595305    0.569735   0.572038   0.99937   
perform       0.358704  0.633465   0.382403    0.344281    0.36969  0.875433   
train         0.347704  0.507483   0.365873    0.335841    0.34564  0.733119   
detect         0.37174  0.507127   0.392079    0.376069   0.372999  0.836253   
use           0.216124  0.528599   0.250173    0.231518    0.24211  0.829421   

                model   dataset     class     learn      perform     train  \
test         0.839523  0.762406  0.649276  0.746572     0.588301  0.585634   
peopl        0.779141   0.70194  0.601803  0.666994      0.45761  0.451924   
covid        0.966408  0.983197  0.747855  0.853802     0.731261  0.687667   
symptom      0.715226  0.628236  0.525632  0.578567     0.360208  0.355796   
travel       0.708682  0.633547  0.508831  0.557406     0.366167  0.340721   
fever        0.719424  0.639605  0.515307  0.566059     0.358704  0.347704   
infect        0.77787  0.849464  0.711211  0.635198     0.633465  0.507483   
contact       0.72059  0.644935    0.5262  0.595305     0.382403  0.365873   
respiratori  0.705195   0.64003  0.509852  0.569735     0.344281  0.335841   
worker       0.711361  0.630681  0.509444  0.572038      0.36969   0.34564   
imag          1.13159  0.935924   0.94137   0.99937     0.875433  0.733119   
model               0  0.950057  0.851355  0.890738     0.595767  0.723461   
dataset      0.950057         0  0.788631  0.882097     0.729462  0.754527   
class        0.851355  0.788631         0  0.726501     0.654866  0.591455   
learn        0.890738  0.882097  0.726501         0     0.629472  0.603452   
perform      0.595767  0.729462  0.654866  0.629472  7.45058e-09  0.442139   
train        0.723461  0.754527  0.591455  0.603452     0.442139         0   
detect       0.721344   0.75098  0.605089  0.659699     0.472107  0.446377   
use          0.749587  0.656879  0.547929  0.578637      0.39981  0.438878   

               detect          use  
test         0.567781     0.500907  
peopl        0.476563     0.354012  
covid        0.744875     0.638247  
symptom      0.387151     0.229288  
travel       0.369656     0.234633  
fever         0.37174     0.216124  
infect       0.507127     0.528599  
contact      0.392079     0.250173  
respiratori  0.376069     0.231518  
worker       0.372999      0.24211  
imag         0.836253     0.829421  
model        0.721344     0.749587  
dataset       0.75098     0.656879  
class        0.605089     0.547929  
learn        0.659699     0.578637  
perform      0.472107      0.39981  
train        0.446377     0.438878  
detect              0     0.418005  
use          0.418005  3.72529e-09  
Pairwise-distance
                    test     peopl     covid    symptom    travel      fever  \
test         1.05367e-08   0.71754   1.00834   0.456049  0.537165   0.506135   
peopl            0.71754         0  0.790457   0.495751  0.533887   0.493232   
covid            1.00834  0.790457         0   0.826913  0.825439   0.806747   
symptom         0.456049  0.495751  0.826913          0  0.182534  0.0640092   
travel          0.537165  0.533887  0.825439   0.182534         0       0.16   
fever           0.506135  0.493232  0.806747  0.0640092      0.16          0   
infect          0.793405  0.765842  0.871985   0.595059  0.560405   0.572065   
contact         0.605784  0.542459   0.75418   0.307258  0.348043   0.282971   
respiratori     0.521191  0.494308  0.800593  0.0811905  0.157644  0.0196609   
worker           0.52954  0.504351  0.786483   0.136227  0.145368   0.091882   
imag                   0         0         0          0         0          0   
model           0.688797  0.546779  0.765173   0.273475   0.24722   0.218312   
dataset                0         0         0          0         0          0   
class           0.688693  0.529488  0.768325   0.277696  0.252525   0.222494   
learn           0.689364  0.558379  0.759092   0.279514  0.261134   0.226237   
perform                0         0         0          0         0          0   
train           0.687486  0.548146  0.763672   0.273804  0.247161   0.218255   
detect          0.682172  0.549082  0.765596   0.275253  0.252343   0.224381   
use             0.696814  0.563204  0.751145   0.287686  0.265965   0.236063   

                  infect   contact respiratori       worker imag        model  \
test            0.793405  0.605784    0.521191      0.52954    0     0.688797   
peopl           0.765842  0.542459    0.494308     0.504351    0     0.546779   
covid           0.871985   0.75418    0.800593     0.786483    0     0.765173   
symptom         0.595059  0.307258   0.0811905     0.136227    0     0.273475   
travel          0.560405  0.348043    0.157644     0.145368    0      0.24722   
fever           0.572065  0.282971   0.0196609     0.091882    0     0.218312   
infect       1.05367e-08  0.649338    0.564415     0.514095    0     0.522983   
contact         0.649338         0    0.277489     0.296168    0     0.295401   
respiratori     0.564415  0.277489           0    0.0818984    0     0.199051   
worker          0.514095  0.296168   0.0818984  3.72529e-09    0     0.185527   
imag                   0         0           0            0    0            0   
model           0.522983  0.295401    0.199051     0.185527    0  6.58545e-10   
dataset                0         0           0            0    0            0   
class           0.526194  0.302597    0.203141     0.186351    0    0.0560273   
learn           0.531784  0.320956    0.207761     0.193265    0    0.0855665   
perform                0         0           0            0    0            0   
train           0.526924  0.297096    0.198951     0.182754    0    0.0584029   
detect          0.522705  0.303703    0.204984     0.187367    0    0.0555047   
use             0.541837  0.296915    0.218261     0.203807    0     0.105329   

            dataset        class      learn perform      train     detect  \
test              0     0.688693   0.689364       0   0.687486   0.682172   
peopl             0     0.529488   0.558379       0   0.548146   0.549082   
covid             0     0.768325   0.759092       0   0.763672   0.765596   
symptom           0     0.277696   0.279514       0   0.273804   0.275253   
travel            0     0.252525   0.261134       0   0.247161   0.252343   
fever             0     0.222494   0.226237       0   0.218255   0.224381   
infect            0     0.526194   0.531784       0   0.526924   0.522705   
contact           0     0.302597   0.320956       0   0.297096   0.303703   
respiratori       0     0.203141   0.207761       0   0.198951   0.204984   
worker            0     0.186351   0.193265       0   0.182754   0.187367   
imag              0            0          0       0          0          0   
model             0    0.0560273  0.0855665       0  0.0584029  0.0555047   
dataset           0            0          0       0          0          0   
class             0  6.58545e-10  0.0854419       0  0.0547559  0.0512827   
learn             0    0.0854419          0       0  0.0834261  0.0818999   
perform           0            0          0       0          0          0   
train             0    0.0547559  0.0834261       0          0  0.0552351   
detect            0    0.0512827  0.0818999       0  0.0552351          0   
use               0      0.10707   0.125391       0   0.105332   0.101862   

                  use  
test         0.696814  
peopl        0.563204  
covid        0.751145  
symptom      0.287686  
travel       0.265965  
fever        0.236063  
infect       0.541837  
contact      0.296915  
respiratori  0.218261  
worker       0.203807  
imag                0  
model        0.105329  
dataset             0  
class         0.10707  
learn        0.125391  
perform             0  
train        0.105332  
detect       0.101862  
use                 0  

(-0.05094283504320366, 0.59601, -0.24801716533077636)

2. 2-3 paragraphs

covid-science.txt

def load_data(path,file_name):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    document = ''
    i=0
    with open( os.path.join(path, file_name) ,"r",encoding="utf8") as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for word in words:
                i=i+1
                document = document+word+' '
                if i == 200:
                    documents_list.append(document)
                    document,i = '',0
                    if len(documents_list) == 2:
                        break
    print("Total Number of Documents:",len(documents_list))
    #titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count_science=load_data("","covid-science.txt")

Number of words in text file : 120257
Total Number of Documents: 2

clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images', 'tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herrera', 'abstract', 'currently', 'coronavirus', 'disease', 'covid', 'infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans', 'chest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'medical', 'centers', 'hence', 'cases', 'images', 'become', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networks', 'great', 'potential', 'building', 'covid', 'triage', 'systems', 'detecting', 'covid', 'patients', 'especially', 'patients', 'severity', 'unfortunately', 'current', 'databases', 'allow', 'building', 'systems', 'highly', 'heterogeneous', 'biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved', 'recent', 'covid', 'classification', 'models', 'under', 'close', 'collaboration', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levels', 'manuscript', 'received', 'september', 'revised', 'october', 'accepted', 'november']
['publication', 'november', 'current', 'version', 'december', 'supported', 'project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid', 'spanish', 'ministry', 'science', 'technology', 'under', 'project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal', 'programme', '18136', 'gómez', 'supported', 'programme', 'fpu16', '04765', 'charte', 'supported', 'programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research', 'council', 'grant', 'agreement', '647038', 'biodesert', 'project', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'andalusian', 'research', 'institute', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', '36310', 'spain', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'atlanttic', 'research', 'center']

# LSA Model
number_of_topics=2
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_science_p=create_gensim_lsa_model(clean_text,number_of_topics,words)

dict(model_science.show_topic(0, topn=5))

{'support': -0.3289947178827887,
 'granada': -0.248140372090372,
 'covid': -0.2276958139915442,
 'programm': -0.21932981192185907,
 'research': -0.21932981192185907}

from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph8.png')

# LSA Model

a=pd.DataFrame()

for i in range(number_of_topics):
    words_covid_science =dict(model_science.show_topic(i, topn=words))
    b=pd.DataFrame(words_covid_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled9.png')

cosine-similarity
            support   granada      covid   programm   research
support           1  0.956969 -0.0794645          1          1
granada    0.956969         1   0.213226   0.956969   0.956969
covid    -0.0794645  0.213226          1 -0.0794645 -0.0794645
programm          1  0.956969 -0.0794645          1          1
research          1  0.956969 -0.0794645          1          1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig9.png')

covid-non-science.txt

document_list,titles,word_count_nscience=load_data("","covid-non-science.txt")

Number of words in text file : 8713
Total Number of Documents: 2

clean_text=preprocess_data(document_list)

['covid', 'recovery', 'covid', 'reality', 'reuters', 'published', 'updated', 'jones', 'london', 'reuters', 'world', 'shares', 'inched', 'towards', 'month', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'nagging', 'coronavirus', 'nerves', 'market', 'rally', 'fuelled', 'record', 'numbers', 'largely', 'blown', 'itself', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'decade', 'stimulus', 'ensured', 'optimism', 'remained', 'chinese', 'shares', 'charged', 'their', 'highest', 'level', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'traders', 'surprise', 'currency', 'commodity', 'markets', 'subdued', 'after', 'otherwise', 'strong', 'confidence', 'sensitive', 'stalwarts', 'copper', 'sterling', 'australian', 'dollar', 'which', 'struggled', 'friday', 'think', 'infection', 'rates', 'fears', 'localised', 'lockdowns', 'doused', 'enthusiasm', 'societe', 'generale', 'strategist', 'jukes', 'three', 'elements', 'vaccine', 'hopes', 'decent', 'places', 'return']
['infection', 'rates', 'which', 'nervous', 'against', 'basket', 'currencies', 'dollar', 'slightly', 'early', 'london', 'trading', 'still', 'firmly', 'track', 'biggest', 'weekly', 'since', 'first', 'though', 'gained', 'against', 'swiss', 'franc', 'versus', 'sometimes', 'commodity', 'driven', 'norwegian', 'crown', 'futures', 'volumes', 'lower', 'usual', 'markets', 'holiday', 'friday', 'independence', 'nonfarm', 'payrolls', 'surged', 'million', 'above', 'average', 'forecast', 'million', 'thanks', 'rises', 'hospitality', 'sectors', 'economists', 'noted', 'there', 'caveats', 'upbeat', 'headline', 'figures', 'number', 'permanent', 'losers', 'continued', 'increasing', 'million', 'while', 'unemployment', 'remains', 'chunky', 'percentage', 'points', 'above', 'february', 'level', 'deutsche', 'analysis', 'unemployment', 'behind', 'developed', 'market', 'peers', 'barring', 'canada', 'recovery', 'faces', 'headwinds', 'surge', 'coronavirus', 'infections', 'prompts', 'states', 'delay', 'cases']

# LSA Model
number_of_topics=2
words=word_count_nscience
model_nscience=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science =dict(model_nscience.show_topic(0, topn=words))##dict and encoding matrix values

dict(model_nscience.show_topic(0, topn=5))

{'market': -0.29865735592426446,
 'covid': -0.21979867961934133,
 'friday': -0.18596179123202247,
 'infect': -0.15212490284470356,
 'month': -0.1465324530795608}

from wordcloud import WordCloud
text = dict(model_nscience.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph9.png')

# LSA Model
a=pd.DataFrame()

for i in range(number_of_topics):
    words_covid_non_science =dict(model_nscience.show_topic(i, topn=words))
    b=pd.DataFrame(words_covid_non_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_nscience.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled10.png')

cosine-similarity
          market     covid    friday    infect     month
market         1  0.741138  0.986861  0.813347  0.741138
covid   0.741138         1  0.839872  0.212223         1
friday  0.986861  0.839872         1  0.708661  0.839872
infect  0.813347  0.212223  0.708661         1  0.212223
month   0.741138         1  0.839872  0.212223         1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='y',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig10.png')

covid-psychology.txt

document_list,titles,word_count_psy=load_data("","covid-psychology.txt")

Number of words in text file : 86550
Total Number of Documents: 2

clean_text=preprocess_data(document_list)

['international', 'sociology', 'author', 'article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissions', '0268580920948807', 'journals', 'sagepub', 'psychology', 'politics', 'covid', 'misinfodemics', 'people', 'believe', 'misinfodemics', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'lahore', 'pakistan', 'abstract', 'misinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adverse', 'health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate', 'evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects', 'misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier', 'about', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article', 'review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article', 'shows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive', 'crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications', 'development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywords', 'conspiracy', 'theories', 'covid']
['health', 'communication', 'misinformation', 'mistrust', 'science', 'mechanisms', 'misinfodemics', 'modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surrounding', 'corresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', '948807iss0010', '0268580920948807international', 'sociologymukhtar', 'research', 'article2020', 'article', 'international', 'sociology', 'covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern', 'globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during', 'lockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'become', 'major', 'source', 'interaction', 'holman', 'covid', 'pandemic', 'outbreak', 'escalated', 'challenges', 'healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerable', 'misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science', 'times', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'false', 'context', 'manipulated']

# LSA Model
number_of_topics=2
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_psy_p = create_gensim_lsa_model(clean_text,number_of_topics,words)

dict(model_psy.show_topic(0, topn=5))

{'misinfodem': 0.5033743541941375,
 'covid': 0.3629711503323513,
 'articl': 0.21459251935197482,
 'commun': 0.14837863098037746,
 'mechan': 0.14837863098037676}

from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph28.png')

# LSA Model

a=pd.DataFrame()

for i in range(number_of_topics):
    words_psychology =dict(model_psy.show_topic(i, topn=words))
    b=pd.DataFrame(words_psychology,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled11.png')

cosine-similarity
           misinfodem     covid    articl    commun    mechan
misinfodem          1  0.976353  0.993795  0.755596  0.755596
covid        0.976353         1  0.946251  0.879335  0.879335
articl       0.993795  0.946251         1  0.678051  0.678051
commun       0.755596  0.879335  0.678051         1         1
mechan       0.755596  0.879335  0.678051         1         1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig11.png')

mantel_test(5)

Pairwise-distance
           misinfodem     covid    articl commun mechan   support    granada  \
misinfodem          0         0         0      0      0         0          0   
covid               0         0  0.378443      0      0  0.628979    0.47364   
articl              0  0.378443         0      0      0  0.423546   0.266413   
commun              0         0         0      0      0         0          0   
mechan              0         0         0      0      0         0          0   
support             0  0.628979  0.423546      0      0         0   0.168269   
granada             0   0.47364  0.266413      0      0  0.168269          0   
programm            0  0.545218  0.293443      0      0  0.133207  0.0775087   
research            0  0.545218  0.293443      0      0  0.133207  0.0775087   

             programm   research  
misinfodem          0          0  
covid        0.545218   0.545218  
articl       0.293443   0.293443  
commun              0          0  
mechan              0          0  
support      0.133207   0.133207  
granada     0.0775087  0.0775087  
programm            0          0  
research            0          0  
Pairwise-distance
           misinfodem     covid    articl    commun    mechan support granada  \
misinfodem          0  0.191325  0.296807  0.422574  0.422574       0       0   
covid        0.191325         0  0.160587  0.236438  0.236438       0       0   
articl       0.296807  0.160587         0  0.173786  0.173786       0       0   
commun       0.422574  0.236438  0.173786         0         0       0       0   
mechan       0.422574  0.236438  0.173786         0         0       0       0   
support             0         0         0         0         0       0       0   
granada             0         0         0         0         0       0       0   
programm            0         0         0         0         0       0       0   
research     0.533903  0.353741    0.2551  0.121646  0.121646       0       0   

           programm  research  
misinfodem        0  0.533903  
covid             0  0.353741  
articl            0    0.2551  
commun            0  0.121646  
mechan            0  0.121646  
support           0         0  
granada           0         0  
programm          0         0  
research          0         0  

(-0.12443276806076518, 0.65413, -0.5168730477188538)

mantel_test(10)

Pairwise-distance
           misinfodem     covid    articl commun mechan health psycholog  \
misinfodem          0         0         0      0      0      0         0   
covid               0         0  0.378443      0      0      0         0   
articl              0  0.378443         0      0      0      0         0   
commun              0         0         0      0      0      0         0   
mechan              0         0         0      0      0      0         0   
health              0         0         0      0      0      0         0   
psycholog           0         0         0      0      0      0         0   
strategi            0         0         0      0      0      0         0   
misinform           0         0         0      0      0      0         0   
inform              0         0         0      0      0      0         0   
support             0  0.628979  0.423546      0      0      0         0   
granada             0   0.47364  0.266413      0      0      0         0   
programm            0  0.545218  0.293443      0      0      0         0   
research            0  0.545218  0.293443      0      0      0         0   
tabik               0  0.436747   0.19981      0      0      0         0   
garcía              0  0.331184  0.138524      0      0      0         0   
project             0  0.511267   0.22973      0      0      0         0   
decsai              0  0.511267   0.22973      0      0      0         0   
chart               0  0.407471  0.133207      0      0      0         0   

           strategi misinform inform   support    granada   programm  \
misinfodem        0         0      0         0          0          0   
covid             0         0      0  0.628979    0.47364   0.545218   
articl            0         0      0  0.423546   0.266413   0.293443   
commun            0         0      0         0          0          0   
mechan            0         0      0         0          0          0   
health            0         0      0         0          0          0   
psycholog         0         0      0         0          0          0   
strategi          0         0      0         0          0          0   
misinform         0         0      0         0          0          0   
inform            0         0      0         0          0          0   
support           0         0      0         0   0.168269   0.133207   
granada           0         0      0  0.168269          0  0.0775087   
programm          0         0      0  0.133207  0.0775087          0   
research          0         0      0  0.133207  0.0775087          0   
tabik             0         0      0   0.22973  0.0666033   0.112805   
garcía            0         0      0  0.336537   0.168269    0.22561   
project           0         0      0   0.19981  0.0903451  0.0666033   
decsai            0         0      0   0.19981  0.0903451  0.0666033   
chart             0         0      0  0.293443   0.133207   0.168269   

             research      tabik     garcía    project     decsai      chart  
misinfodem          0          0          0          0          0          0  
covid        0.545218   0.436747   0.331184   0.511267   0.511267   0.407471  
articl       0.293443    0.19981   0.138524    0.22973    0.22973   0.133207  
commun              0          0          0          0          0          0  
mechan              0          0          0          0          0          0  
health              0          0          0          0          0          0  
psycholog           0          0          0          0          0          0  
strategi            0          0          0          0          0          0  
misinform           0          0          0          0          0          0  
inform              0          0          0          0          0          0  
support      0.133207    0.22973   0.336537    0.19981    0.19981   0.293443  
granada     0.0775087  0.0666033   0.168269  0.0903451  0.0903451   0.133207  
programm            0   0.112805    0.22561  0.0666033  0.0666033   0.168269  
research            0   0.112805    0.22561  0.0666033  0.0666033   0.168269  
tabik        0.112805          0   0.112805  0.0775087  0.0775087  0.0666033  
garcía        0.22561   0.112805          0   0.181739   0.181739  0.0775087  
project     0.0666033  0.0775087   0.181739          0          0   0.112805  
decsai      0.0666033  0.0775087   0.181739          0          0   0.112805  
chart        0.168269  0.0666033  0.0775087   0.112805   0.112805          0  
Pairwise-distance
           misinfodem     covid     articl       commun       mechan  \
misinfodem          0  0.191325   0.296807     0.422574     0.422574   
covid        0.191325         0   0.160587     0.236438     0.236438   
articl       0.296807  0.160587          0     0.173786     0.173786   
commun       0.422574  0.236438   0.173786            0            0   
mechan       0.422574  0.236438   0.173786            0            0   
health       0.422574  0.236438   0.173786  1.86265e-09  2.63418e-09   
psycholog    0.364939   0.24088  0.0802933     0.191551     0.191551   
strategi     0.364939   0.24088  0.0802933     0.191551     0.191551   
misinform    0.510199  0.318875   0.290918     0.118949     0.118949   
inform        0.41268  0.243293    0.12755    0.0789346    0.0789346   
support             0         0          0            0            0   
granada             0         0          0            0            0   
programm            0         0          0            0            0   
research     0.533903  0.353741     0.2551     0.121646     0.121646   
tabik               0         0          0            0            0   
garcía              0         0          0            0            0   
project             0         0          0            0            0   
decsai              0         0          0            0            0   
chart               0         0          0            0            0   

                 health  psycholog   strategi    misinform     inform support  \
misinfodem     0.422574   0.364939   0.364939     0.510199    0.41268       0   
covid          0.236438    0.24088    0.24088     0.318875   0.243293       0   
articl         0.173786  0.0802933  0.0802933     0.290918    0.12755       0   
commun      1.86265e-09   0.191551   0.191551     0.118949  0.0789346       0   
mechan      2.63418e-09   0.191551   0.191551     0.118949  0.0789346       0   
health                0   0.191551   0.191551     0.118949  0.0789346       0   
psycholog      0.191551          0          0      0.30895   0.118949       0   
strategi       0.191551          0          0      0.30895   0.118949       0   
misinform      0.118949    0.30895    0.30895  3.72529e-09   0.191551       0   
inform        0.0789346   0.118949   0.118949     0.191551          0       0   
support               0          0          0            0          0       0   
granada               0          0          0            0          0       0   
programm              0          0          0            0          0       0   
research       0.121646   0.233213   0.233213     0.145904    0.12755       0   
tabik                 0          0          0            0          0       0   
garcía                0          0          0            0          0       0   
project               0          0          0            0          0       0   
decsai                0          0          0            0          0       0   
chart                 0          0          0            0          0       0   

           granada programm  research tabik garcía project decsai chart  
misinfodem       0        0  0.533903     0      0       0      0     0  
covid            0        0  0.353741     0      0       0      0     0  
articl           0        0    0.2551     0      0       0      0     0  
commun           0        0  0.121646     0      0       0      0     0  
mechan           0        0  0.121646     0      0       0      0     0  
health           0        0  0.121646     0      0       0      0     0  
psycholog        0        0  0.233213     0      0       0      0     0  
strategi         0        0  0.233213     0      0       0      0     0  
misinform        0        0  0.145904     0      0       0      0     0  
inform           0        0   0.12755     0      0       0      0     0  
support          0        0         0     0      0       0      0     0  
granada          0        0         0     0      0       0      0     0  
programm         0        0         0     0      0       0      0     0  
research         0        0         0     0      0       0      0     0  
tabik            0        0         0     0      0       0      0     0  
garcía           0        0         0     0      0       0      0     0  
project          0        0         0     0      0       0      0     0  
decsai           0        0         0     0      0       0      0     0  
chart            0        0         0     0      0       0      0     0  

(-0.16750679664761384, 0.88844, -1.1558042446362033)

mantel_test_n(5)

Pairwise-distance
         market     covid friday infect month   support    granada   programm  \
market        0         0      0      0     0         0          0          0   
covid         0         0      0      0     0  0.628979    0.47364   0.545218   
friday        0         0      0      0     0         0          0          0   
infect        0         0      0      0     0         0          0          0   
month         0         0      0      0     0         0          0          0   
support       0  0.628979      0      0     0         0   0.168269   0.133207   
granada       0   0.47364      0      0     0  0.168269          0  0.0775087   
programm      0  0.545218      0      0     0  0.133207  0.0775087          0   
research      0  0.545218      0      0     0  0.133207  0.0775087          0   

           research  
market            0  
covid      0.545218  
friday            0  
infect            0  
month             0  
support    0.133207  
granada   0.0775087  
programm          0  
research          0  
Pairwise-distance
            market        covid    friday    infect      month support  \
market           0     0.207135  0.121064  0.179156    0.20674       0   
covid     0.207135  5.26836e-09  0.151144  0.302288  0.0895781       0   
friday    0.121064     0.151144         0  0.151144   0.103568       0   
infect    0.179156     0.302288  0.151144         0   0.243141       0   
month      0.20674    0.0895781  0.103568  0.243141          0       0   
support          0            0         0         0          0       0   
granada          0            0         0         0          0       0   
programm         0            0         0         0          0       0   
research         0            0         0         0          0       0   

         granada programm research  
market         0        0        0  
covid          0        0        0  
friday         0        0        0  
infect         0        0        0  
month          0        0        0  
support        0        0        0  
granada        0        0        0  
programm       0        0        0  
research       0        0        0  

(-0.25569736166843005, 1.0, -1.131609129366209)

mantel_test_n(10)

Pairwise-distance
         market     covid friday infect month three reuter share year copper  \
market        0         0      0      0     0     0      0     0    0      0   
covid         0         0      0      0     0     0      0     0    0      0   
friday        0         0      0      0     0     0      0     0    0      0   
infect        0         0      0      0     0     0      0     0    0      0   
month         0         0      0      0     0     0      0     0    0      0   
three         0         0      0      0     0     0      0     0    0      0   
reuter        0         0      0      0     0     0      0     0    0      0   
share         0         0      0      0     0     0      0     0    0      0   
year          0         0      0      0     0     0      0     0    0      0   
copper        0         0      0      0     0     0      0     0    0      0   
support       0  0.628979      0      0     0     0      0     0    0      0   
granada       0   0.47364      0      0     0     0      0     0    0      0   
programm      0  0.545218      0      0     0     0      0     0    0      0   
research      0  0.545218      0      0     0     0      0     0    0      0   
tabik         0  0.436747      0      0     0     0      0     0    0      0   
garcía        0  0.331184      0      0     0     0      0     0    0      0   
project       0  0.511267      0      0     0     0      0     0    0      0   
decsai        0  0.511267      0      0     0     0      0     0    0      0   
chart         0  0.407471      0      0     0     0      0     0    0      0   

           support    granada   programm   research      tabik     garcía  \
market           0          0          0          0          0          0   
covid     0.628979    0.47364   0.545218   0.545218   0.436747   0.331184   
friday           0          0          0          0          0          0   
infect           0          0          0          0          0          0   
month            0          0          0          0          0          0   
three            0          0          0          0          0          0   
reuter           0          0          0          0          0          0   
share            0          0          0          0          0          0   
year             0          0          0          0          0          0   
copper           0          0          0          0          0          0   
support          0   0.168269   0.133207   0.133207    0.22973   0.336537   
granada   0.168269          0  0.0775087  0.0775087  0.0666033   0.168269   
programm  0.133207  0.0775087          0          0   0.112805    0.22561   
research  0.133207  0.0775087          0          0   0.112805    0.22561   
tabik      0.22973  0.0666033   0.112805   0.112805          0   0.112805   
garcía    0.336537   0.168269    0.22561    0.22561   0.112805          0   
project    0.19981  0.0903451  0.0666033  0.0666033  0.0775087   0.181739   
decsai     0.19981  0.0903451  0.0666033  0.0666033  0.0775087   0.181739   
chart     0.293443   0.133207   0.168269   0.168269  0.0666033  0.0775087   

            project     decsai      chart  
market            0          0          0  
covid      0.511267   0.511267   0.407471  
friday            0          0          0  
infect            0          0          0  
month             0          0          0  
three             0          0          0  
reuter            0          0          0  
share             0          0          0  
year              0          0          0  
copper            0          0          0  
support     0.19981    0.19981   0.293443  
granada   0.0903451  0.0903451   0.133207  
programm  0.0666033  0.0666033   0.168269  
research  0.0666033  0.0666033   0.168269  
tabik     0.0775087  0.0775087  0.0666033  
garcía     0.181739   0.181739  0.0775087  
project           0          0   0.112805  
decsai            0          0   0.112805  
chart      0.112805   0.112805          0  
Pairwise-distance
            market        covid    friday    infect      month        three  \
market           0     0.207135  0.121064  0.179156    0.20674      0.20674   
covid     0.207135  5.26836e-09  0.151144  0.302288  0.0895781    0.0895781   
friday    0.121064     0.151144         0  0.151144   0.103568     0.103568   
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month      0.20674    0.0895781  0.103568  0.243141          0            0   
three      0.20674    0.0895781  0.103568  0.243141          0  3.72529e-09   
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share      0.20674    0.0895781  0.103568  0.243141          0  3.72529e-09   
year       0.20674    0.0895781  0.103568  0.243141          0  2.63418e-09   
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Paragraph level

covid-science.txt

def load_data(path,file_name):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    with open( os.path.join(path, file_name) ,"r",encoding='UTF8') as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for line in fin.readlines():
            text = line.strip()
            if text !='':
                documents_list.append(text)
    print("Total Number of Documents:",len(documents_list))
    titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count_science=load_data("","covid-science.txt")

Number of words in text file : 120257
Total Number of Documents: 14379

clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnet']
['methodology', 'predicting', 'covid']
['based', 'chest', 'images']
['tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte']
['guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova']
['olmedo', 'sánchez', 'herrera']
['abstract', 'currently', 'coronavirus', 'disease', 'covid']
['infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans']
['chest', 'images', 'computed', 'tomography']
['scanners', 'testing', 'available']
['medical', 'centers', 'hence', 'cases', 'images']
['become', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networks']
['great', 'potential', 'building', 'covid', 'triage', 'systems']
['detecting', 'covid', 'patients', 'especially', 'patients']
['severity', 'unfortunately', 'current', 'databases', 'allow']
['building', 'systems', 'highly', 'heterogeneous']
['biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved']
['recent', 'covid', 'classification', 'models', 'under']
['close', 'collaboration', 'hospital', 'universitario', 'clínico']
['cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levels']
['manuscript', 'received', 'september', 'revised', 'october']
['accepted', 'november', 'publication', 'november']
['current', 'version', 'december', 'supported']
['project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundación']
['equipos', 'investigación', 'científica', 'covid']
['spanish', 'ministry', 'science', 'technology', 'under']
['project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal']
['programme', '18136', 'gómez', 'supported']
['programme', 'fpu16', '04765', 'charte', 'supported']
['programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research']
['council', 'grant', 'agreement', '647038', 'biodesert', 'project']
['approved', 'provincial', 'research', 'ethics', 'committee', 'granada']
['corresponding', 'author', 'siham', 'tabik']
['tabik', 'gómez', 'sevillano', 'garcía', 'charte', 'suárez']
['luengo', 'herrera', 'andalusian', 'research', 'institute']
['science', 'computational', 'intelligence', 'university', 'granada']
['18071', 'granada', 'spain', 'siham', 'anabelgrios', 'decsai']
['isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz']
['julianlm', 'decsai', 'herrera', 'decsai']
['martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova']
['olmedo', 'sánchez', 'hospital', 'universitario', 'clínico']
['cecilio', 'granada', '36310', 'spain', 'joseluismartin']
['hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail']
['euolm', 'yahoo']
['atlanttic', 'research', 'center', 'telecommunication', 'technologies', 'university', 'galicia', 'spain']
['mreyarea', 'gmail']
['guirado', 'multidisciplinary', 'institute', 'environment']
['studies', 'ramón', 'margalef', 'university', 'alicante', '03690', 'spain']
['geesecillo', 'gmail']
['digital', 'object', 'identifier', '3037127']
['severity', 'normal', 'positive', 'moderate', 'severe', 'covidgr', 'contains', 'positive']
['negative', 'posteroanterior', 'views']
['propose', 'covid', 'smart', 'based', 'network', 'covid', 'sdnet']
['methodology', 'improving', 'generalization', 'capacity']
['covid', 'classification', 'models', 'approach', 'reaches']
['stable', 'results', 'accuracy']
['severe', 'moderate']
['covid', 'severity', 'levels', 'approach', 'could']
['early', 'detection', 'covid', 'covidgr', 'along']
['severity', 'level', 'labels', 'available', 'scientific', 'community', 'through', 'https', 'dasci', 'transferencia']
['covidgr']
['index', 'terms', 'covid', 'convolutional', 'neural', 'networks']
['smart']
['introduction']
[]
['months', 'world', 'witnessing']
['covid', 'pandemic', 'increasingly', 'infecting', 'large']
['people', 'everywhere', 'world', 'trends']
['clear', 'research', 'confirm', 'problem']
['persist', 'until', 'besides', 'prevalence', 'studies', 'conducted']
['several', 'countries', 'reveal', 'proportion', 'population']
['developed', 'antibodies', 'after', 'exposure', 'virus']
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['consists', 'analyzing', 'radiographic', 'images', 'different', 'angles', 'needed', 'equipment', 'assessment']
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['minutes', 'patient', 'addition', 'required']
['decontamination']
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['online', 'available', 'https', 'english', 'elpais', 'society']
['antibody', 'study', 'shows', 'spaniards', 'contracted', 'coronavirus']
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['article', 'access', 'download', 'along', 'rights', 'mining', 'analysis']
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['journal', 'biomedical', 'health', 'informatics', 'december']
['stratification', 'radiological', 'severity', 'covid', 'examples', 'index', 'calculated']
['nasopharyngeal', 'requires', 'specific', 'material']
['equipment', 'which', 'easily', 'accessible', 'takes']
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['essay', 'performed', 'within', 'reported']
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['centers', 'confirmed', 'coronavirus', 'disease', 'normal']
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['correctly', 'diagnosed', 'expert']
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['radiographic', 'assessment', 'edema', 'score']
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['features', 'consolidation', 'ground', 'glass', 'opacities']
['parts', 'depicted', 'based', 'score']
['experts', 'identify', 'level', 'severity', 'infection', 'among']
['severity', 'stages', 'normal', 'moderate']
['severe', 'practice', 'patient', 'classified', 'expert', 'radiologist']
['authorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'xplore', 'restrictions', 'apply']
['tabik', 'covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images']
['normal', 'positive', 'refer', 'these', 'cases']
['normal', 'expert', 'annotation', 'adopted', 'based']
['score']
['automated', 'image', 'analysis', 'learning', 'models']
['great', 'potential', 'optimize', 'images']
['diagnosis', 'covid', 'robust', 'accurate']
['model', 'could', 'serve', 'triage', 'method', 'support']
['medical', 'decision', 'making', 'increasing', 'number', 'recent', 'works']
['claim', 'achieving', 'impressive', 'sensitivities', 'higher']
['expert', 'radiologists', 'these', 'sensitivities']
['covid', 'dataset', 'covid', 'image']
['collection', 'dataset', 'includes', 'small', 'number']
['covid', 'positive', 'cases', 'coming', 'highly', 'heterogeneous']
['sources', 'least', 'countries', 'cases', 'severe', 'patients']
['issue', 'drastically', 'reduces', 'clinical', 'value', 'populate']
['covid', 'healthy', 'classes', 'researchers', 'using']
['images', 'diverse', 'pulmonary', 'disease', 'repositories']
['obtained', 'models', 'clinical', 'value', 'since']
['unable', 'detect', 'patients', 'moderate', 'severity']
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['higher', 'concentration', 'useful', 'information', 'smart']
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['easily', 'customize', 'sources', 'structure']
['output', 'product', 'execution', 'logic', 'processing', 'frequency']
['exception', 'handling', 'addition', 'users', 'modify']
['source', 'extend', 'collecting', 'datasets', 'other']
['purposes', 'support', 'wider', 'studies', 'tasks', 'gency', 'response', 'natural', 'disaster', 'detection', 'saving']
['lives']
['currently', 'limitation', 'quality', 'control']
['validation', 'cannot', 'fully', 'automated', 'because', 'accuracy']
['parsing', 'extracting', 'cannot', 'always', 'guaranteed']
['using', 'current', 'packages', 'hence', 'users', 'intervene']
['quality', 'control', 'process', 'image']
['product', 'quality']
['rapid', 'development', 'parsing', 'images']
['updating', 'component', 'minimize', 'human', 'intervention']
['automation', 'process']

# LSA Model
number_of_topics=20
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_law =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_science_paragraph=create_gensim_lsa_model(clean_text,number_of_topics,words)

dict(model_science.show_topic(0, topn=5))

{'covid': 0.835348015250747,
 'imag': 0.3551974666283513,
 'detect': 0.1421581702026208,
 'model': 0.126714953242308,
 'dataset': 0.10935480954447266}

from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph10.png')

# LSA Model
a=pd.DataFrame()

for i in range(number_of_topics):
    words_science =dict(model_science.show_topic(i, topn=words))
    b=pd.DataFrame(words_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled12.png')

cosine-similarity
              covid         imag      detect        model      dataset
covid             1  -0.00282975  0.00589357  -0.00120735  -0.00234126
imag    -0.00282975            1 -0.00970229 -0.000818478  -0.00606992
detect   0.00589357  -0.00970229           1   0.00510676  -0.00596494
model   -0.00120735 -0.000818478  0.00510676            1  0.000181606
dataset -0.00234126  -0.00606992 -0.00596494  0.000181606            1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *100  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig12.png')

covid-non-science-small.txt

document_list,titles,word_count_nscience=load_data("","covid-non-science.txt")

Number of words in text file : 8713
Total Number of Documents: 387

clean_text=preprocess_data(document_list)

['covid', 'recovery', 'covid', 'reality']
['reuters']
['published', 'updated']
['jones']
['london', 'reuters', 'world', 'shares', 'inched', 'towards', 'month', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'nagging', 'coronavirus', 'nerves']
['market', 'rally', 'fuelled', 'record', 'numbers', 'largely', 'blown', 'itself', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'decade', 'stimulus', 'ensured', 'optimism', 'remained']
['chinese', 'shares', 'charged', 'their', 'highest', 'level', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'traders', 'surprise']
['currency', 'commodity', 'markets', 'subdued', 'after', 'otherwise', 'strong', 'confidence', 'sensitive', 'stalwarts', 'copper', 'sterling', 'australian', 'dollar', 'which', 'struggled', 'friday']
['think', 'infection', 'rates', 'fears', 'localised', 'lockdowns', 'doused', 'enthusiasm', 'societe', 'generale', 'strategist', 'jukes']
['three', 'elements', 'vaccine', 'hopes', 'decent', 'places', 'return', 'infection', 'rates', 'which', 'nervous']
['against', 'basket', 'currencies', 'dollar', 'slightly', 'early', 'london', 'trading', 'still', 'firmly', 'track', 'biggest', 'weekly', 'since', 'first']
['though', 'gained', 'against', 'swiss', 'franc', 'versus', 'sometimes', 'commodity', 'driven', 'norwegian', 'crown']
['futures', 'volumes', 'lower', 'usual', 'markets', 'holiday', 'friday', 'independence']
['nonfarm', 'payrolls', 'surged', 'million', 'above', 'average', 'forecast', 'million', 'thanks', 'rises', 'hospitality', 'sectors']
['economists', 'noted', 'there', 'caveats', 'upbeat', 'headline', 'figures']
['number', 'permanent', 'losers', 'continued', 'increasing', 'million', 'while', 'unemployment', 'remains', 'chunky', 'percentage', 'points', 'above', 'february', 'level', 'deutsche', 'analysis', 'unemployment', 'behind', 'developed', 'market', 'peers', 'barring', 'canada']
['recovery', 'faces', 'headwinds', 'surge', 'coronavirus', 'infections', 'prompts', 'states', 'delay', 'cases', 'reverse', 'plans', 'stores', 'reopen', 'activities', 'resume']
['three', 'dozen', 'states', 'increases', 'covid', 'cases', 'cases', 'florida', 'spiking', 'above']
['nevertheless', 'markets', 'largely', 'overlooking', 'spikes', 'taking', 'overall', 'situation', 'still', 'improving', 'overall']
['german', 'government', 'yields', 'basis', 'points', 'their', 'biggest', 'weekly', 'month', 'though', 'nudged', 'friday', 'riskier', 'italian', 'yields', 'though', 'which', 'their', 'lowest', 'since', 'march']
['prices', 'eased', 'after', 'otherwise', 'solid', 'brent', 'crude', 'barrel', 'while', 'crude', 'dropped', 'barrel', 'around', 'months']
['copper', 'prices', 'poised', 'seventh', 'consecutive', 'weekly', 'their', 'longest', 'winning', 'streak', 'nearly', 'three', 'years', 'despite', 'slight', 'easing', 'after', 'supplier', 'chile', 'assured', 'traders', 'about', 'supply']
['three', 'month', 'copper', 'hovering', 'tonne', 'ploughed', 'march']
['issue', 'hangs', 'markets', 'surge', 'secondary', 'infections', 'trigger', 'second', 'national', 'rather', 'regional', 'shutdowns', 'malcolm', 'freeman', 'director', 'kingdom', 'futures', 'wrote']
['reporting', 'jones']
['newspaper']
['covid', 'vaccine', 'frontrunners']
['reuters']
['published', 'october', 'updated', 'october']
['reuters', 'pfizer', 'could', 'november', 'authorization', 'covid', 'vaccine', 'developing', 'opening', 'possibility', 'vaccine', 'being', 'available', 'united', 'states', 'moderna', 'astrazeneca', 'close', 'behind', 'largest', 'drugmaker', 'likely', 'early', 'their', 'vaccine', 'candidates', 'before', 'potential', 'vaccines', 'being', 'developed', 'tested', 'globally', 'covid', 'pandemic', 'human', 'trials', 'according', 'world', 'health', 'organization', 'following', 'tracks', 'candidates', 'final', 'stages', 'testing', 'separate', 'factbox', 'coronavirus', 'vaccines', 'under', 'development', 'click', 'company', 'stage', 'development', 'expected', 'doses', 'pfizer', 'stage', 'trials', 'underway', 'option', 'biontech', 'pfizer', 'earlier', 'october', 'could', 'emergency', 'authorization', 'november', 'indicating', 'vaccine', 'could', 'potentially', 'available', 'additional', 'canada', 'specified', 'japan', 'moderna', 'completed', 'enrollment', 'option', 'subjects', 'stage', 'trial', 'additional', 'interim', 'results', 'trial', 'expected', 'november', 'japan', 'distributed', 'takeda', 'canada', 'specified', 'johnson', 'johnson', 'large', 'vaccine', 'trial', 'resume', 'after', 'paused', 'under', 'subsequent', 'agreement', 'illness', 'study', 'volunteer', 'results', 'trial', 'expected', 'option', 'early', 'additional', 'canada', 'japan', 'applicable', 'astrazeneca', 'stage', 'trials', 'underway', 'supply', 'deals', 'countries', 'oxford', 'university', 'trial', 'vaccine', 'candidate', 'doses', 'pause', 'since', 'after', 'patient', 'trial', 'results', 'expected', 'coming', 'weeks', 'commitment', 'deliver', 'total', 'least', 'additional', 'italy', 'germany', 'netherlands', 'france', 'additional', 'canada', 'japan', 'sinovac', 'biotech', 'stage', 'trials', 'brazil', 'vaccine', 'approved', 'emergency', 'indonesia', 'turkey', 'underway', 'china', 'supply', 'prelim', 'expected', 'november', 'indonesia', 'least', 'doses', 'before', 'gamaleya', 'research', 'stage', 'trial', 'covid', 'people', 'inoculated', 'institute', 'vaccine', 'sputnik', 'underway', 'russia', 'supply', 'deals', 'since', 'prelim', 'countries', 'including', 'india', 'expected', 'november', 'brazil', 'saudi', 'arabia', 'doses', 'cansino', 'biologics', 'vaccine', 'candidate', 'final', 'stage', 'mexico', 'doses', 'early', 'trials', 'approved', 'chinese', 'military', 'stage', 'trial', 'underway', 'pakistan', 'sinopharm', 'group', 'stage', 'trials', 'expects', 'produce', 'underway', 'billion', 'doses', 'reporting', 'dania', 'nadeem', 'mrinalika', 'bengaluru', 'editing', 'ankur', 'banerjee', 'samuel']
['newspaper']
['giants', 'another', 'covid']
['reuters']
['published', 'november', 'updated', 'november']
['giants', 'another', 'covid']
['monday', 'night', 'notified', 'player', 'tested', 'positive', 'covid', 'giants', 'announced', 'tuesday', 'morning', 'player', 'immediately', 'isolated', 'contact', 'tracing', 'process', 'initiated', 'close', 'contacts', 'staff', 'members', 'identified', 'informed', 'remain', 'today']
['giants', 'entering', 'their', 'second', 'player', 'positive', 'covid']
['prior', 'playing', 'tampa', 'buccaneers', 'offensive', 'lineman', 'hernandez', 'tested', 'positive']
['hernandez', 'spent', 'nearly', 'weeks', 'reserve', 'covid', 'returned', 'played', 'sunday', 'victory', 'philadelphia', 'eagles']
['field', 'level', 'media']
['newspaper']
['covid', 'antivirals', 'taskforce', 'launched']
['media']
['published', 'april', 'updated', 'april']
['covid', 'treatments', 'being', 'sought', 'government', 'experts', 'speed', 'recovery', 'people', 'become', 'infected', 'virus']
['government', 'taskforce', 'supercharge', 'search', 'antiviral', 'medications', 'covid']
['prime', 'minister', 'boris', 'johnson', 'group', 'medicines', 'covid', 'tracks']
['hoped', 'antivirals', 'could', 'reduce', 'infections', 'limit', 'impact', 'variants']
['protect', 'people', 'cannot', 'vaccines', 'those', 'fully', 'protected', 'after', 'having']
['officials', 'bring', 'treatments', 'online']
['could', 'people', 'covid', 'those', 'close', 'contact', 'could', 'offered', 'antiviral', 'medication', 'offered', 'tablet', 'infection', 'spreading', 'speed', 'recovery']
['antivirals', 'taskforce', 'which', 'appoint', 'chair', 'search', 'promising', 'potential', 'antiviral', 'medicines', 'which', 'taken']
['taskforce', 'support', 'development', 'drugs', 'through', 'clinical', 'trials', 'manufacturing', 'opportunities']
['antiviral', 'drugs', 'medication', 'specifically', 'treating', 'viral', 'infections', 'killing', 'preventing', 'growth', 'viruses']
['success', 'vaccination', 'programme', 'demonstrated', 'achieve', 'bring', 'together', 'brightest', 'minds', 'johnson']
['antivirals', 'taskforce', 'develop', 'innovative', 'treatments', 'covid', 'tracks']
['these', 'could', 'provide', 'another', 'vital', 'defence', 'against', 'future', 'increase', 'infections', 'lives']
['health', 'secretary', 'hancock', 'medicines', 'vital', 'weapon', 'protect', 'loved', 'terrible', 'virus']
['modelled', 'success', 'vaccines', 'therapeutics', 'taskforces', 'which', 'played', 'crucial', 'response', 'pandemic', 'bringing', 'together', 'supercharge', 'search', 'antiviral', 'treatments', 'autumn']
['government', 'chief', 'scientific', 'adviser', 'patrick', 'vallance', 'speed', 'which', 'vaccines', 'therapeutics', 'dexamethasone', 'identified', 'deployed', 'against', 'covid', 'critical', 'pandemic', 'response']
['antivirals', 'tablet', 'another', 'response']
['could', 'protect', 'those', 'protected', 'ineligible', 'vaccines']
['could', 'another', 'layer', 'defence', 'variants', 'concern']
['nikita', 'kanani', 'medical', 'director', 'primary', 'england', 'research', 'reality', 'record', 'speed', 'during', 'pandemic', 'taskforce', 'identify', 'convenient', 'treatments', 'patients', 'covid']
['commenting', 'announcement', 'penny', 'visiting', 'professor', 'pharmaceutical', 'medicine', 'kings', 'college', 'london', 'antiviral', 'treatment', 'influenza', 'shown', 'reduce', 'hospitalisation', 'prevent', 'death', 'epidemic', 'pandemic', 'waves', 'similar', 'preferably', 'simple', 'antiviral', 'suitable', 'community', 'range', 'interventions', 'critical', 'enable', 'remain', 'covid', 'pandemic', 'event', 'emergence', 'viral', 'variants', 'insensitive', 'vaccines']
['there', 'number', 'early', 'phase', 'trials', 'looking', 'antiviral', 'medications']
['newspaper']
['african', 'covid', 'deaths']
[]
['published', 'february', 'updated', 'february']
['africa', 'thursday', 'recorded', 'deaths', 'covid', 'milestone', 'likely', 'understate', 'continent', 'billion', 'people', 'battles', 'second', 'infections']
['countries', 'region', 'death', 'reported', 'cases', 'according', 'tally']
['continent', 'relatively', 'spared', 'pandemic', 'except', 'oceania', 'reach', 'threshold', 'deaths', 'which', 'europe', 'crossed', 'april']
['south', 'africa', 'worst', 'african', 'country', 'rolled', 'testing', 'campaign', 'start', 'pandemic']
['country', 'recorded', 'nearly', 'million', 'cases', 'deaths']
['those', 'figures', 'based', 'daily', 'reports', 'communicated', 'health', 'authorities', 'reflect', 'fraction', 'actual', 'health', 'specialists']
['cases', 'clearly', 'under', 'reported', 'because', 'access', 'healthcare', 'facilities', 'under', 'reporting', 'milder', 'cases', 'south', 'african', 'virologist', 'barry', 'schoub', 'member', 'scientific', 'council', 'south', 'african', 'ministry', 'health']
['understaffed', 'health', 'facilities', 'means', 'meant', 'african', 'countries', 'unable', 'testing']
['countries', 'mainly', 'tests', 'capitals', 'further', 'moves', 'urban', 'centres', 'there', 'tests', 'explained', 'french', 'epidemiologist', 'emmanuel', 'baron', 'doctors', 'without', 'borders']
['disease', 'unnoticed', 'asymptomatic', 'patients', 'symptoms', 'confused', 'others', 'added']
['covid', 'found', 'pawpaw']
['zimbabwe', 'country', 'devastated', 'economy', 'mismanaged', 'health', 'system', 'hospitals', 'filled', 'covid', 'patients', 'exhausted', 'doctors', 'overwhelmed', 'nurses', 'official', 'number', 'cases', 'remains']
['tanzania', 'stopped', 'testing', 'after', 'claiming', 'found', 'positive', 'covid', 'pawpaw', 'quail', 'tanzanian', 'government', 'released', 'official', 'figures', 'april']
['someone', 'continent', 'would', 'deaths', 'infection', 'probably', 'would', 'believed', 'nkengasong', 'africa', 'director', 'centers', 'disease', 'control', 'preventions', 'reporters', 'thursday']
['africa', 'however', 'significantly', 'lower', 'europe', 'world', 'affected', 'region', 'deaths', 'recorded', 'other', 'regions', 'badly', 'latin', 'america', 'deaths', 'united', 'states', 'canada', 'deaths']
['after', 'sharp', 'increase', 'january', 'africa', 'figures', 'fallen', 'sharply', 'weeks', 'seven', 'continent', 'recorded', 'deaths', 'percent', 'previous']
['height', 'pandemic', 'january', 'continent', 'deaths']
['disaster']
['while', 'coronavirus', 'figures', 'clearly', 'underestimated', 'health', 'disaster', 'africa', 'baron']
['several', 'studies', 'antibodies', 'which', 'possible', 'detect', 'whether', 'person', 'recovered', 'previously', 'exposed', 'virus', 'underway', 'african', 'countries', 'should', 'provide', 'better', 'impact', 'pandemic', 'region']
['south', 'africa', 'where', 'almost', 'latest', 'cases', 'attributed', 'variant', 'virus', 'known', 'contagious', 'which', 'spread', 'widely', 'represents', 'nearly', 'deaths', 'reported', 'cases', 'continent']
['other', 'african', 'countries', 'affected', 'egypt', 'deaths', 'cases', 'morocco', 'deaths', 'cases']
['south', 'africa', 'country', 'covid', 'fatalities', 'continent', 'counting', 'deaths', 'inhabitants', 'ahead', 'tunisia', 'deaths', 'eswatini']
['lagging', 'behind', 'vaccination', 'continent', 'leading', 'industrial', 'powerhouse', 'administered', 'first', 'vaccines', 'wednesday']
['globally', 'covid', 'caused', 'million', 'infections', 'million', 'deaths', 'since', 'start', 'epidemic', 'wuhan', 'china', 'december']
[]
['newspaper']
['covid', 'hotspots', 'industry']
['australian', 'associated', 'press']
['published', 'august', 'updated', 'august']
['fitness', 'industry', 'perception', 'covid', 'hotspots', 'saying', 'there', 'transmission', 'virus', 'linked']
['fitness', 'australia', 'chief', 'executive', 'barrie', 'elvish', 'fitness', 'australia', 'gathered', 'random', 'sample', 'operators', 'proves']
['since', 'reopened', 'there', 'million', 'visits', 'recorded', 'community', 'transmission']
['cannot', 'restaurants', 'where', 'cluster', 'cases', 'started', 'spread', 'however', 'still', 'being', 'categorised', 'these', 'other', 'entertainment', 'venues', 'negative', 'light', 'statement', 'thursday']
['however', 'misinformation', 'negative', 'commentary', 'portrayed', 'health', 'fitness', 'facilities', 'hotspots', 'covid', 'resulting', 'people', 'being', 'scared', 'exercise', 'their', 'despite', 'additional', 'precautions', 'stringent', 'hygiene', 'practices']
['fitness', 'australia', 'gathered', 'electronic', 'swipes', 'members', 'access', 'which', 'could', 'sophisticated', 'contact', 'tracing', 'should', 'required']
['elvish', 'there', 'reported', 'cases', 'where', 'member', 'visited', 'while', 'infected', 'covid', 'health', 'authorities', 'identified', 'cases', 'transmission']
['problem', 'actually', 'solution', 'getting', 'australians', 'active', 'often', 'prevent', 'lifestyle', 'related', 'disease', 'elvish']
['urging', 'government', 'fitness', 'australia', 'industry', 'ensure', 'health', 'safety', 'community']
['newspaper']
['covid', 'victims', 'maintain', 'immunity']
['australian', 'associated', 'press']
['published', 'november', 'updated', 'november']
['australian', 'researchers', 'discovered', 'patients', 'infected', 'covid', 'retain', 'immunity', 'against', 'virus', 'disease', 'least', 'eight', 'months']
['research', 'strongest', 'evidence', 'vaccines', 'against', 'virus', 'periods']
['previous', 'studies', 'found', 'first', 'antibodies', 'produced', 'human', 'after', 'infection', 'waned', 'after', 'first', 'months', 'raising', 'concerns', 'people', 'could', 'quickly', 'immunity']
['research', 'allays', 'those', 'concerns']
['study', 'result', 'collaboration', 'associate', 'professor', 'menno', 'monash', 'university', 'published', 'monday', 'preprint', 'server', 'medrxiv']
['researchers', 'found', 'specific', 'within', 'human', 'immune', 'system', 'memory', 'remembers', 'infection', 'virus', 'challenged', 'again', 'through', 'exposure', 'virus', 'triggers', 'protective', 'immune', 'response', 'through', 'rapid', 'production', 'protective', 'antibodies']
['researchers', 'recruited', 'covid', 'patients', 'blood', 'samples', 'between', 'infection', 'infection']
['other', 'studies', 'looking', 'antibody', 'response', 'researchers', 'found', 'antibodies', 'against', 'virus', 'started', 'after', 'infection']
['however', 'patients', 'continued', 'memory', 'cells', 'recognised', 'components', 'virus', 'spike', 'nucleocapsid', 'proteins']
['these', 'virus', 'specific', 'memory', 'cells', 'present', 'eight', 'months', 'after', 'infection']
['associate', 'professor', 'results', 'efficacy', 'vaccine', 'against', 'virus', 'explained', 'there', 'examples', 'genuine', 'reinfection', 'millions', 'people', 'tested', 'positive', 'virus', 'globally']
['these', 'results', 'important', 'because', 'definitively', 'patients', 'infected', 'covid', 'virus', 'retain', 'immunity', 'against', 'virus', 'disease']
['black', 'cloud', 'hanging', 'potential', 'protection', 'could', 'provided', 'covid', 'vaccine', 'gives', 'vaccine', 'vaccines', 'developed', 'provide', 'protection']
['newspaper']
['origins', 'covid']
['reuters']
['published', 'january', 'updated', 'january']
['shanghai', 'reuters', 'world', 'health', 'organization', 'works', 'china', 'investigate', 'origins', 'covid', 'following', 'factbox', 'looks', 'about', 'pandemic', 'began']
['china', 'origins']
['coronavirus', 'causes', 'covid', 'known', 'first', 'identified', 'central', 'chinese', 'wuhan', 'january', 'scientists', 'believe', 'still', 'likely', 'originated', 'china']
['peter', 'embarek', 'expert', 'safety', 'zoonotic', 'diseases', 'wuhan', 'offered', 'first', 'solid', 'clues', 'about', 'transmission', 'covid', 'investigation', 'would', 'start', 'there']
['though', 'studies', 'suggested', 'covid', 'present', 'italy', 'spain', 'earlier', 'might', 'responsible', 'spike', 'pneumonia', 'cases', 'france', 'researchers', 'believe', 'could', 'entered', 'wuhan', 'europe']
['closest', 'relative', 'nature', 'ratg13', 'virus', 'which', 'discovered', 'horseshoe', 'southwest', 'china', 'yunnan', 'province', 'genetic', 'match', 'between', 'makes', 'highly', 'likely', 'covid', 'originated', 'colonies', 'china', 'southwest', 'border', 'regions']
['huanan', 'market']
['initial', 'cluster', 'infections', 'traced', 'huanan', 'seafood', 'market', 'wuhan', 'leading', 'assume', 'patient', 'probably', 'trader', 'exposed', 'contaminated', 'products']
['version', 'events', 'simplistic', 'explain', 'patterns', 'infection', 'wuhan', 'elsewhere']
['earliest', 'reported', 'cases', 'connection', 'market', 'wuhan', 'residents', 'hospitalised', 'turned', 'covid', 'huanan']
['chinese', 'researchers', 'there', 'types', 'circulating', 'wuhan', 'which', 'associated', 'market']
['scientists', 'virus', 'unusually', 'adapted', 'rapid', 'human', 'transmission', 'making', 'unlikely', 'first', 'human', 'contact', 'seafood', 'market']
['intermediary', 'species']
['investigators', 'intermediary', 'species', 'allowed', 'original', 'humans']
['preliminary', 'scientific', 'papers', 'china', 'identified', 'snakes', 'potential', 'candidates', 'similar', 'coronavirus', 'infections', 'found', 'pangolins', 'illegally', 'trafficked', 'china']
['scholars', 'believe', 'there', 'intermediary', 'species', 'virus', 'transmitted', 'directly', 'humans', 'possibly', 'multiple', 'occasions']
['first', 'people', 'infected', 'likely', 'traders', 'droppings', 'traditional', 'chinese', 'medicine', 'could', 'carried', 'huanan', 'seafood', 'market', 'causing', 'superspreader', 'event', 'allowed', 'pandemic', 'begin']
['function']
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['australian', 'associated', 'press']
['published', 'october', 'updated', 'october']
['sydney', 'portugal', 'community', 'being', 'forced', 'seven', 'after', 'failing', 'comply', 'covid', 'regulations', 'after', 'three', 'warnings']
['liquor', 'gaming', 'director', 'compliance', 'dimitri', 'argeres', 'portugal', 'marrickville', 'fourth', 'business', 'closed', 'covid', 'safety', 'breaches']
['close', 'wednesday']
['inspectors', 'visited', 'three', 'times', 'observed', 'breaches', 'relating', 'their', 'covid', 'safety', 'overbooking', 'groups', 'gaming', 'machine', 'spacing', 'inadequate', 'check', 'processes', 'argeres', 'tuesday']
['since', 'closure', 'order', 'issued', 'however', 'things', 'fined', 'longer', 'breaches', 'example', 'bookings', 'people', 'rather', 'while', 'guests', 'spaced', 'least', 'metres', 'apart', 'gaming', 'machines']
['changed', 'having', 'robust', 'digital', 'check', 'processes', 'allow', 'effective', 'contact', 'tracing', 'event', 'positive', 'visited', 'venue', 'argeres']
['service', 'covid', 'check', 'businesses', 'registered', 'covid', 'anyone', 'check', 'using', 'service', 'account']
['inspectors', 'liquor', 'gaming', 'trading', 'safework', 'conducted', 'covid', 'safety', 'visits', 'issued', 'penalties', 'worth', 'temporarily', 'closed', 'businesses']
['newspaper']
['tests', 'underway', 'covid', 'treatment']
['australian', 'associated', 'press']
['published', 'august', 'updated', 'august']
['australian', 'researchers', 'testing', 'treatment', 'coronavirus', 'provide', 'relief', 'those', 'infected', 'covid', 'limit', 'spread']
['professor', 'david', 'morris', 'international', 'adapted', 'cancer', 'treatment', 'known', 'bromac', 'could', 'prevent', 'virus', 'moving', 'lungs', 'carriers', 'spreading', 'other', 'people']
['trial', 'coronavirus', 'patients', 'being', 'established', 'melbourne', 'hospital', 'could', 'start', 'month']
['bromac', 'under', 'development', 'years', 'cancer', 'treatment', 'includes', 'components', 'together', 'dissolve', 'spike', 'covid', 'rendering', 'unable', 'infect', 'other', 'cells']
['agents', 'pineapple', 'enzyme', 'tested', 'after', 'observed', 'eating', 'pineapples', 'resistant', 'particular', 'gastro', 'conditions']
['taken', 'development', 'decade', 'asked', 'whether', 'adapted', 'treating', 'people', 'infected', 'covid', 'professor', 'david', 'morris', 'statement', 'monday']
['results', 'renders', 'covid', 'spike', 'ineffective', 'stopping', 'infecting', 'other', 'cells']
['results', 'treatment', 'confine', 'covid', 'throat', 'prevent', 'infection', 'infected', 'patients', 'passing', 'virus']
['hoped', 'infected', 'person', 'could', 'medication', 'first', 'signs', 'covid', 'virus', 'tracks']
['could', 'provide', 'effective', 'saving', 'lives', 'protecting', 'vulnerable', 'workers', 'preventing', 'hospitalisation', 'getting', 'people', 'infected', 'covid', 'returning', 'their', 'everyday', 'lives']
['researchers', 'stress', 'treatment', 'vaccine', 'covid', 'could', 'tandem', 'treatment', 'prevention', 'measure']

# LSA Model
number_of_topics=20
words=word_count_nscience
model_nscience=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science =dict(model_nscience.show_topic(0, topn=words))##dict and encoding matrix values

dict(model_nscience.show_topic(0, topn=5))

{'trial': -0.4614933940742868,
 'vaccin': -0.3811205893046194,
 'stage': -0.32791335485243206,
 'expect': -0.23283549464984898,
 'dose': -0.19987756716304975}

from wordcloud import WordCloud
text = dict(model_nscience.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph11.png')

# LSA Model

a=pd.DataFrame()

for i in range(number_of_topics):
    words_non_science =dict(model_nscience.show_topic(i, topn=words))
    b=pd.DataFrame(words_non_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_nscience.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled13.png')

cosine-similarity
           trial    vaccin     stage    expect      dose
trial          1  0.236693  0.967238  0.899454  0.756959
vaccin  0.236693         1  0.254672  0.299765  0.630181
stage   0.967238  0.254672         1  0.952112  0.790247
expect  0.899454  0.299765  0.952112         1  0.796809
dose    0.756959  0.630181  0.790247  0.796809         1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='y',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig13.png')

covid-psychology-small.txt

document_list,titles,word_count_psy=load_data("","covid-psychology.txt")

Number of words in text file : 86550
Total Number of Documents: 9007

clean_text=preprocess_data(document_list)

['international', 'sociology']
[]
['author']
['article', 'reuse', 'guidelines']
['sagepub', 'journals', 'permissions']
['0268580920948807']
['journals', 'sagepub']
['psychology', 'politics']
['covid', 'misinfodemics']
['people']
['believe', 'misinfodemics']
['sonia', 'mukhtar']
['university', 'management', 'technology', 'lahore', 'pakistan']
['abstract']
['misinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adverse']
['health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate']
['evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects']
['misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier']
['about', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article']
['review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects']
['responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article']
['shows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product']
['psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive']
['crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations']
['psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications']
['development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health']
['keywords']
['conspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'science']
['mechanisms', 'misinfodemics']
['modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surrounding']
['corresponding', 'author']
['sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore']
['punjab', '54770', 'pakistan']
['email', 'sonia', 'mukhtar12', 'gmail']
['948807iss0010', '0268580920948807international', 'sociologymukhtar']
['research', 'article2020']
['article']
['international', 'sociology']
['covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus']
['covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern']
['globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin']
['unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during']
['lockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication']
['become', 'major', 'source', 'interaction', 'holman']
['covid', 'pandemic', 'outbreak', 'escalated', 'challenges']
['healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerable']
['misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories']
['claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science']
['times', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'false']
['context', 'manipulated', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease']
['mukhtar', '2020a']
['covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms']
['become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated']
['these', 'rumors', 'caused', 'several', 'mishaps', 'across', 'world', 'instance']
['after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'overdosed']
['cholorquine', 'after', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 'adebayo', 'there', 'failure']
['distinction', 'between', 'empirically', 'based', 'scientific', 'truths', 'fabricated', 'unconfirmed']
['science', 'conspiracy', 'theories', 'among', 'general', 'public', 'reports', 'reduce', 'legitimacy', 'scientific', 'discoveries', 'regarding', 'vaccine', 'covid']
['create', 'social', 'stigma', 'resulting', 'xenophobia', 'chinese', 'sentiment', 'racism', 'marginalization', 'reduced', 'compliance', 'adherence', 'quarantine', 'adverse', 'health']
['psychosocial', 'impacts', 'aguilera', '2020a', '2020b', 'these', 'considerations', 'become', 'exacerbated', 'during', 'lockdown', 'leading', 'people', 'fringe']
['popular', 'opinion', 'spend', 'social', 'media', 'people', 'trying']
['sense', 'their', 'changed', 'lives', 'proclaimed', 'celebrities', 'identified', 'social']
['media', 'stars', 'politicians', 'mainstream', 'media', 'public', 'figures', 'propagating', 'their']
['subjective', 'interpretations', 'events', 'situation', 'covid', 'pandemic', 'misinfodemics', 'lederer']
['scientific', 'studies', 'previous', 'disease', 'outbreaks', 'demonstrated', 'misinformation', 'represents', 'secondary', 'challenge', 'public', 'health', 'efforts', 'controlling', 'epidemic', 'pandemic', 'earnshaw', 'kalichman', 'individuals', 'endorse']
['misinformation', 'about', 'disease', 'likely', 'follow', 'public', 'health', 'instructions', 'during']
['ebola', 'outbreak', 'respondents', 'credence', 'conspiracy', 'theories']
['alleged', 'would', 'likely', 'support', 'during', 'disease', 'outbreak']
['national', 'cancer', 'institute', 'study', 'montanaro', 'conducted']
['respondents', 'reported', 'trust', 'medical', 'healthcare', 'professionals', 'contrast', 'study', 'where', 'majority', 'respondents', 'reported', 'mistrust', 'information']
['about', 'covid', 'current', 'administration', 'mainstream', 'media', 'outlets']
['times']
['mukhtar']
['psychology', 'misinfodemics']
['adaptive', 'emotion', 'which', 'serves', 'mobilize', 'energy', 'towards', 'potential', 'actual']
['perceived', 'threat', 'mertens', 'however', 'excessive']
['detrimental', 'effects', 'individual', 'mental', 'health', 'problems', 'anxiety']
['phobia', 'community', 'level', 'hoarding', 'panic', 'shopping', 'xenophobia']
['insufficient', 'cause', 'individual', 'incompliance', 'nonadherence', 'towards', 'quarantine', 'isolation', 'community', 'reckless', 'implementation']
['policies', 'ignores', 'risks', 'socioeconomic', 'infrastructure', 'likewise', 'interpersonal']
['safety', 'measures', 'mitigate', 'certain', 'threats', 'transmission', 'disease', 'paradoxically', 'enhance', 'transmission', 'excessive', 'health', 'anxiety', 'psychosocial']
['issues', 'similarly', 'social', 'safety', 'measures', 'lockdowns', 'curfews', 'control', 'transmission', 'prolonged', 'stringent', 'measures', 'could', 'negative', 'consequences', 'socioeconomic', 'impact', 'mental', 'health', 'impact']
['efforts', 'misinfodemics', 'process', 'increased', 'because', 'adverse']
['effects', 'public', 'health', 'communication', 'encourage', 'adoption', 'sustainable']
['preventive', 'measures', 'manage', 'social', 'physical', 'distancing', 'psychological', 'health']
['resilience', 'socioeconomic', 'conditions', 'address', 'stigma', 'prejudice', 'discrimination']
['inequalities', 'mukhtar', '2020b', 'among', 'various', 'factors', 'psychological', 'vulnerability', 'mukhtar', '2020c', 'mukhtar', 'mahmood', 'propensity', 'follow', 'popular']
['opinion', 'excessive', 'social', 'media', 'presence', 'predisposition', 'anxiety']
['uncertainty', 'intolerance', 'unpredictability', 'coronavirus', 'brings', 'levels', 'uncertainty', 'inability', 'uncertainty', 'exponentially', 'higher', 'causing', 'anxiety']
['health', 'related', 'worry', 'situation', 'exacerbated', 'exposure', 'plethora']
['information', 'including', 'misinformation', 'disinformation', 'about', 'impending']
['threat', 'mainstream', 'media', 'social', 'media', 'alike', 'rosser', 'perceived', 'threat']
['information', 'elevates', 'repeated', 'engagement', 'trauma', 'related', 'media', 'content']
['several', 'hours', 'daily', 'culminates', 'acute', 'stress', 'emotional', 'distress', 'consequently']
['either', 'increase', 'virus', 'cause', 'insensitivity', 'towards', 'course']
['identification', 'monitoring', 'internet', 'centrifugal', 'clarification', 'filter']
['accuracy', 'content', 'become', 'challenging', 'increasing', 'number', 'people']
['social', 'media', 'platforms', 'challenging', 'problems', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'social', 'media', 'emulsify', 'false']
['misleading', 'click', 'content', 'world', 'health', 'organization', 'launched']
['mythbuster', 'feature', 'website', 'countermeasure', 'spread', 'unauthentic']
['social', 'media', 'people', 'reported', 'highest', 'media', 'exposure', 'reported', 'higher']
['acute', 'stress', 'holman', 'media', 'exposure', 'accumulates', 'emerging', 'threats']
['repeated', 'exposure', 'these', 'events', 'increase', 'symptoms', 'distress', 'worry', 'fixation']
['mentality', 'misplaced', 'conviction', 'people', 'vulnerable', 'media', 'exposure', 'distress']
['alongside', 'amount', 'media', 'exposure', 'content', 'exposure', 'matters']
['exposure', 'tragic', 'events', 'graphic', 'images', 'conspiracy', 'theories', 'violence', 'could']
['instigate', 'posttraumatic', 'stress', 'future', 'which', 'personal']
['functioning', 'recent', 'years', 'mainstream', 'media', 'electronic', 'print', 'social', 'media']
['established', 'themselves', 'beyond', 'government', 'channel', 'global']
['international', 'sociology']
['population', 'developed', 'affinity', 'channels', 'particular', 'while', 'older']
['population', 'mainly', 'relies', 'traditional', 'channels', 'youth', 'social', 'media']
['tiktok', 'instagram', 'youtube', 'twitter', 'facebook', 'whatsapp', 'social', 'media']
['become', 'conduit', 'spreading', 'rumors', 'deliberate', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'personally', 'motivated', 'anecdotes', 'appeal', 'followers']
['attract', 'attention', 'create', 'panic', 'liang', 'proclaimed', 'celebrities', 'media', 'stars', 'become', 'influential', 'large', 'number', 'people', 'solely']
['their', 'content', 'merchant', 'social', 'media', 'posts', 'circulating']
['platforms', 'endorsed', 'multiple', 'social', 'media', 'account', 'users', 'encourage', 'optimal']
['personal', 'functioning', 'maximum', 'productivity', 'business', 'oriented', 'activities', 'downplaying', 'intensity', 'traumatic', 'event', 'certain', 'posts']
['quarantine', 'skill', 'hustle', 'started', 'knowledge']
['never', 'lacked', 'lacked', 'discipline', 'circulating', 'various', 'social', 'media', 'accounts']
['prompting', 'people', 'utilize', 'their', 'learning', 'things', 'skills', 'exacerbate']
['worry', 'already', 'anxiety', 'prone', 'individuals', 'place', 'psychological', 'pressure', 'productive', 'rather', 'messages', 'quarantine', 'skill']
['hustle', 'started', 'knowledge', 'doing', 'should', 'disseminated', 'social', 'media', 'individuals', 'realize', 'during', 'intense', 'traumatic', 'event', 'everyone', 'equally', 'endowed', 'strength', 'coping', 'strategies']
['problem', 'solving', 'skills', 'transform', 'trauma', 'positive', 'emotion', 'which']
['source', 'shame', 'guilt']
['there', 'coercive', 'impression', 'percolating', 'among', 'individuals', 'regarding', 'lockdown']
['people', 'should', 'assume', 'holiday', 'utilize', 'their', 'productively', 'engage', 'occupational', 'academic', 'activities', 'psychological', 'pressure']
['further', 'aggravated', 'feelings', 'guilt', 'shame', 'regret', 'sadness', 'anger', 'internalized']
['emotions', 'being', 'overwhelmed', 'negative', 'unrealistic', 'expectations', 'perceived', 'sense', 'failure', 'mukhtar', '2020a', '2020b', 'psychological', 'pressure', 'compete', 'peers', 'achieving', 'maximum', 'tasks', 'producing', 'occupational', 'academic']
['outcomes', 'harnessing', 'followers', 'subscribers', 'media', 'accounts']
['downplaying', 'trauma', 'others', 'devastating', 'effects', 'mental']
['wellbeing']
['lockdown', 'resulting', 'isolation', 'quarantine', 'social', 'distancing', 'removed']
['normal', 'leisure', 'might', 'utilized', 'improved', 'personal', 'functioning']
['collective', 'traumatic', 'event', 'which', 'poses', 'serious', 'threat', 'people', 'resulted']
['lives', 'displacement', 'individuals', 'mukhtar', 'mukhtar', 'mukhtar']
['covid', 'individual', 'collective', 'traumatic', 'event', 'directly']
['indirectly', 'affected', 'every', 'individual', 'world', 'efforts', 'should', 'directed', 'towards']
['minimizing', 'negative', 'effects', 'traumatic', 'covid', 'pandemic', 'survivors']
['people', 'going', 'through', 'interpersonal', 'traumatic', 'events', 'addition', 'collective']
['trauma', 'covid', 'domestic', 'violence', 'gender', 'based', 'violence', 'abuse', 'mukhtar', '2020d']
['financial', 'burden', 'loneliness', 'emotional', 'behavioral', 'problems', 'grief', 'bereavement']
['losing', 'family', 'mental', 'health', 'issues', 'physical', 'injuries', 'fatalities']
['isolated', 'people', 'facing', 'psychological', 'issues', 'require', 'trauma', 'focused', 'psychological']
['support', 'mental', 'health', 'psychological', 'support', 'guidance', 'treatment', 'intervention']
['information', 'psychological', 'cyber', 'counseling', 'smartphone']
['mukhtar']
['instance', 'mental', 'health', 'hotlines', 'professional', 'seeking', 'behaviors', 'should']
['encouraged', 'endorsed', 'related', 'barriers', 'stigma', 'marginalization', 'discrimination', 'shaming', 'phobias', 'should', 'discouraged', 'through', 'government', 'driven', 'programs']
['evidence', 'based', 'treatment', 'models']
['emerging', 'pandemics', 'command', 'intellectual', 'incapacity', 'decision', 'making']
['degree', 'irrationality', 'bombarded', 'conflicting', 'opinions', 'conspiracy', 'theories', 'empirical', 'interpretation', 'origins', 'impacts', 'result', 'moral', 'judgments']
['based', 'religious', 'cultural', 'beliefs', 'emergence', 'garnered', 'plethora']
['moral', 'judgments', 'orbiting', 'around', 'sexual', 'morality', 'substance', 'abuse', 'arrival']
['blame', 'directed', 'towards', 'primitive', 'farming', 'practices', 'guangzhou']
['unprecedented', 'covid', 'pandemic', 'welcomed', 'magnitude', 'misinfodemics']
['multitude', 'fronts', 'ophir', 'richtel', 'psychological', 'predisposition', 'reject']
['authorized', 'information', 'through', 'denial', 'byproduct', 'founded', 'mistrust']
['tendency', 'major', 'social', 'political', 'events', 'through', 'conspiracies', 'partisan', 'motivations', 'byproduct', 'conspiracy', 'thinking', 'likely', 'explanatory', 'factors', 'understanding', 'believes', 'covid', 'misinfodemics', 'association', 'between']
['conspiracy', 'thinking', 'defense', 'mechanism', 'denial', 'could', 'potential', 'reason']
['refusal', 'corrective', 'actions', 'several', 'health', 'related', 'cases', 'carey']
['misinfodemics', 'could', 'motivated', 'various', 'factors', 'epistemic', 'desire', 'causal']
['explanation', 'subjective', 'certainty', 'existential', 'desire', 'control', 'security']
['social', 'desire', 'maintain', 'positive', 'image', 'group', 'douglas']
['stigmatization', 'labeling', 'scapegoating', 'swiftly', 'follow', 'narrative', 'which']
['invokes', 'vernacular', 'human', 'conflict', 'waging', 'against', 'killer', 'virus', 'indomitable', 'invisible', 'threat', 'enemy', 'armed', 'vaccine', 'flinging', 'victims', 'quarantine']
['camps', 'losing', 'sense', 'proportion', 'ability', 'mitigate', 'reasonable']
['knowledge', 'based', 'measures', 'challenge', 'normalize', 'perceived', 'threat', 'infection']
['mechanism', 'misinfodemics', 'engrossed', 'silos', 'public', 'information', 'which']
['inept', 'limiting', 'risks', 'instance', 'social', 'theme', 'focusing', 'socioeconomic', 'disruption', 'scientific', 'theme', 'focusing', 'medical', 'health', 'communication', 'pandemic', 'theme', 'focusing', 'state', 'global', 'response', 'populations']
['likely', 'affected', 'emerging', 'disease', 'pandemic', 'simultaneous', 'misinfodemics']
['include', 'those', 'where', 'there', 'disproportionally', 'inadequate', 'health', 'literacy', 'disadvantaged', 'socioeconomic', 'groups', 'migrants', 'ethnic', 'minorities', 'vulnerable', 'groups']
['including', 'older', 'people', 'people', 'chronic', 'health', 'conditions', 'people', 'disability']
['mukhtar', '2020e', 'rowlands']
['politics', 'coronavirus']
['conspiracy', 'theories', 'heavily', 'influenced', 'geopolitics', 'spread', 'regarding', 'origin']
['scale', 'prevention', 'treatment', 'diagnosis', 'disease', 'covid', 'being', 'viral']
['bioweapon', 'genetically', 'engineered', 'rogue', 'government', 'racist', 'genocidal']
['agenda', 'economic', 'psychological', 'chinese', 'biological', 'weapon']
['conspiracy', 'theory', 'which', 'spread', 'throughout', 'united', 'states', 'united', 'kingdom']
['india', 'ukraine', 'biological', 'weapon', 'conspiracy', 'theory', 'spread', 'throughout']
['russia', 'china', 'philippines', 'venezuela', 'muslims']
['international', 'sociology']
['espionage', 'population', 'control', 'scheme', 'medical', 'misinformation', 'vaccine', 'preexistence', 'cocaine', 'african', 'resistance', 'vegetarian', 'immunity', 'methanol']
['other', 'views', 'endorsed', 'presidents', 'governments', 'public', 'figures', 'abound', 'causing']
['misinfodemics', 'incorrect', 'information', 'about', 'virus', 'which', 'poses', 'risks']
['global', 'scale', 'mccarthy']
['meanwhile', 'covid', 'could', 'positively', 'viewed', 'through', 'partisan']
['right', 'nationalism', 'pandemonium', 'allegations', 'coronavirus']
['politics', 'misinfodemics', 'censorship', 'pathological', 'nationalism', 'multitude']
['governments', 'introduced', 'policy', 'blame', 'shifting', 'launching', 'their', 'powerrivalry', 'statements', 'revolving', 'around', 'illogical', 'reasoning', 'narrative', 'national']
['immunity', 'towards', 'covid', 'there', 'claims', 'biological', 'weapon']
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['twelve', 'clusters', 'found', 'different', 'colors', 'biggest', 'circle', 'showed', 'strongest']
['authorship', 'respective', 'countries']
['cluster', 'represented', 'color', 'shows', 'strong', 'authorship', 'among']
['argentina', 'botswana', 'brazil', 'chile', 'colombia', 'dominican', 'republic', 'guatemala']
['mexico', 'panama', 'paraguay', 'puerto', 'uruguay', 'cluster', 'represented']
['maroon', 'color', 'comprises', 'india', 'indonesia', 'lebanon', 'zeeland', 'nigeria']
['pakistan', 'portugal', 'serbia', 'singapore', 'tunisia', 'color', 'cluster', 'encompasses']
['australia', 'bangladesh', 'canada', 'china', 'finland', 'japan', 'macau', 'philippines']
['russia', 'federation', 'countries', 'present', 'similar', 'clusters', 'strong', 'authorships', 'ijbpsy', 'nawaz']
[]
['figure', 'authorship', 'among', 'countries']
['journals']
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['journals', 'publish', 'frequently', 'psychology', 'research', 'perspective']
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['papers', 'covid', 'first', 'seven', 'months']
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['theory', 'research', 'practice', 'policy', 'remains', 'productive', 'journal', 'terms']
['number', 'publications', 'covid', 'aspects', 'psychology', 'while', 'irish']
['journal', 'psychological', 'medicine', 'remains', 'productive', 'publications']
['suggest', 'these', 'facts', 'future', 'researchers', 'should', 'consult', 'these', 'journals']
['their', 'seminal']
['table', 'journals']
['journal', 'title', 'publication']
['psychological', 'trauma', 'theory', 'research', 'practice', 'policy']
['irish', 'journal', 'psychological', 'medicine']
['social', 'anthropology']
['asian', 'journal', 'psychiatry']
['counselling', 'psychology', 'quarterly']
['journal', 'trauma']
['journal', 'humanistic', 'psychology']
['journal', 'affective', 'disorders']
['nature', 'human', 'behaviour']
['journal', 'psychotherapy', 'integration', '13ijbpsy', 'nawaz']
[]
['figure', 'bibliographic', 'coupling', 'journals']
['occurrence', 'keywords']
['table', 'figure', 'display', 'occurring', 'keywords', 'using']
['published', 'documents', 'occurrence', 'keywords', 'frequently', 'occur', 'studied']
['documents', 'given', 'psychological', 'aspect', 'infectious', 'covid', 'virus', 'causes', 'mental']
['health', 'problems', 'depression', 'anxiety', 'stress', 'traumatic', 'stress', 'disorder']
['future', 'research', 'trends', 'around', 'these', 'occurring', 'words', 'explore', 'about']
['phenomenon']
['table', 'occurring', 'keywords']
['keywords', 'occurrences', 'total', 'strength']
['covid']
['pandemic']
['coronavirus']
['mental', 'health']
['anxiety']
['trauma']
['depression']
['stress']
['covid', 'pandemic']
['30ijbpsy', 'nawaz']
[]
[]
['figure', 'occurrence', 'keywords']
['discussion', 'psychological', 'interventions']
['evidence', 'suggests', 'covid', 'consequences', 'patient', 'families']
['society', 'scientist', 'biologist', 'continuing', 'their', 'efforts']
['pandemic', 'social', 'psychological', 'aspects', 'should', 'ignored', 'however', 'since']
['resources', 'devoted', 'occupied', 'biomedical', 'research', 'hence', 'current']
['state', 'psychology', 'research', 'bibliometric', 'analysis']
['psychological', 'research', 'first', 'seven', 'months', 'pandemic', 'substantiated', 'claim']
['bibliometric', 'analysis', 'showed', 'there', 'studies', 'psychological']
['consequences', 'covid', 'keeping', 'researchers', 'should', 'forward']
['formulate', 'interventions', 'practical', 'theoretical', 'research', 'should', 'initiated']
['crisis', 'strengthen', 'mental', 'psychological', 'health', 'psychological', 'cries']
['should', 'taken', 'public', 'health', 'emergencies', 'cooperation', 'between', 'community', 'health']
['services', 'mental', 'health', 'institutions', 'should', 'decoupled', 'studies']
['confirmed', 'individuals', 'experienced', 'public', 'health']
['emergencies', 'still', 'varying', 'degrees', 'stress', 'disorders', 'after', 'event']
['cured', 'discharged', 'hospital', 'indicating', 'these', 'individuals', 'should']
['ignored', 'cheng', 'there', 'systematic', 'studies', 'ijbpsy', 'nawaz']
[]
['interventions', 'psychological', 'problems', 'hence', 'present']
['suggestions', 'practitioners', 'researchers', 'consider', 'formulating', 'psychological']
['intervention', 'conducting', 'psychological', 'research']
['immediate', 'support', 'system', 'should', 'established', 'early', 'weeks', 'novel']
['coronavirus', 'awareness', 'programs', 'should', 'conducted', 'through', 'devices', 'television']
['radio', 'mobile', 'phone', 'internet', 'resources', 'there', 'should', 'audio', 'video', 'highlighted']
['message', 'attracts', 'attention', 'community', 'programs', 'based', 'psycho', 'education']
['covid', 'disease', 'course', 'precautionary', 'measures', 'symptoms', 'management']
['programs', 'telecast', 'healthcare', 'mental', 'health', 'professionals', 'expert']
['interviews', 'messages', 'regarding', 'disease', 'course', 'severity', 'clinical', 'symptoms', 'place']
['treatment', 'other', 'factors', 'classify', 'individuals', 'management', 'interventions']
['should', 'addressed', 'venerable', 'groups', 'communities', 'children', 'elderly']
['immigrant', 'workers']
['universities', 'institutes', 'should', 'online', 'courses', 'platforms', 'provide']
['counseling', 'services', 'patients', 'their', 'family', 'members', 'people', 'suspected']
['infection', 'disease', 'underneath', 'isolation', 'community', 'psychological', 'health']
['services', 'should', 'primary', 'mental', 'health', 'concerned', 'although', 'since']
['complicated', 'strategies', 'heavy', 'burden', 'workloads', 'insufficient', 'training']
['psychiatry', 'clinical', 'psychology', 'community', 'health', 'services', 'always']
['diminish', 'psychological', 'distress', 'patients', 'specialized', 'comprising', 'mental']
['health', 'services', 'dealing', 'emotional', 'distress', 'other', 'psychological', 'disorders', 'caused']
['epidemics', 'further', 'public', 'health', 'emergencies']
['experience', 'covid', 'survivors', 'prone']
['negative', 'behavioral', 'emotional', 'responses', 'grievance', 'bitterness', 'anger']
['needed', 'dealt', 'sensitive', 'instance', 'tailor', 'psychological', 'screening']
['personal', 'inquiry', 'invitation', 'psychological', 'consultation', 'rather']
['arbitrarily', 'distributing', 'postal', 'questionnaires', 'would', 'appropriate']
['receptive', 'counseling', 'addition', 'facing', 'possible', 'future', 'outbreak', 'psychological']
['preparation', 'stress', 'inoculation', 'meichenbaum', 'needed', 'strengthen']
['sense', 'social', 'support', 'reduce', 'associated', 'social', 'discrimination', 'facilitate']
['socially', 'endorsed', 'communication', 'channels', 'without', 'reducing', 'amount', 'contact']
['brainstorm', 'share', 'possible', 'coping', 'educate', 'adopt', 'realistic', 'threat', 'appraisal']
['booster', 'morale', 'among', 'staff']
['second', 'psychological', 'assessment', 'covid', 'survivors', 'clinicians']
['should', 'include', 'social', 'support', 'negative', 'appraisal', 'perceived', 'impacts', 'positive', 'appraisal']
['traumatic', 'growth', 'efficacy', 'which', 'essential', 'parameters']
['monitoring', 'ongoing', 'psychological', 'perceived', 'physical', 'health', 'covid']
['survivors']
['third', 'these', 'significant', 'psychosocial', 'correlates', 'embed', 'essential', 'values']
['clinical', 'intervention', 'covid', 'survivors', 'instance', 'given', 'significant']
['negative', 'appraisal', 'outcomes', 'cognitive', 'techniques', 'comparing']
['disadvantages', 'reappraising', 'disastrous', 'fears', 'might', 'appropriate', 'particular', 'ijbpsy', 'nawaz']
[]
['maladaptive', 'thinking', 'beliefs', 'elicited', 'further', 'clinicians', 'should', 'actively']
['inquire', 'about', 'impacts', 'covid', 'elicit', 'educate', 'various', 'coping', 'efforts']
['booster', 'coping', 'confidence', 'reinforce', 'their', 'perceived', 'ability']
['impacts', 'allowing', 'survivors', 'review', 'reiterate', 'personal', 'growth']
['traumatic', 'experience', 'create', 'positive', 'meaning', 'reframe', 'impacts']
['possibly', 'alleviate', 'their', 'distress']
['paper']
['fpsyg', '01924', 'september']
['original', 'research']
['published', 'september']
['fpsyg', '01924']
['edited']
['ilhan', 'ozturk']
['university', 'turkey']
['reviewed']
['jasim', 'tariq']
['university', 'pakistan']
['muhammad', 'usman']
['jiangsu', 'university', 'china']
['correspondence']
['khurram', 'shehzad']
['khurramscholar64', 'hotmail']
['specialty', 'section']
['article', 'submitted']
['organizational', 'psychology']
['section', 'journal']
['frontiers', 'psychology']
['received']
['accepted']
['published', 'september']
['citation']
['shehzad', 'xiaoxing']
['rehman', 'ilyas']
['investigating', 'psychology']
['financial', 'markets', 'during', 'covid']
['study']
['european', 'markets']
['front', 'psychol']
['fpsyg', '01924']
['investigating', 'psychology']
['financial', 'markets', 'during', 'covid']
['study']
['european', 'markets']
['khurram', 'shehzad1']
['xiaoxing1']
['muhammad', 'arif2']
['khaliq', 'rehman3']
['muhammad', 'ilyas2']
['school', 'economics', 'management', 'southeast', 'university', 'nanjing', 'china', 'school', 'economics', 'finance']
['jiaotong', 'university', 'china', 'school', 'management', 'wuhan', 'university', 'technology', 'wuhan', 'china']
['novel', 'coronavirus', 'covid', 'imperatively', 'shaken', 'behavior', 'global']
['financial', 'markets', 'study', 'estimated', 'impact', 'covid', 'behavior']
['financial', 'markets', 'europe', 'results', 'revealed', 'returns']
['index', 'greatly', 'affected', 'lockdown', 'owing', 'covid']
['however', 'health', 'crisis', 'generated', 'novel', 'coronavirus', 'significantly', 'decreased']
['stock', 'returns', 'nasdaq', 'composite', 'index', 'results', 'showed']
['economic', 'crisis', 'generated', 'pandemic', 'spain', 'impact']
['compared', 'health', 'crisis', 'itself', 'other', 'italy']
['stock', 'markets', 'affected', 'health', 'crisis', 'contrasted', 'economic']
['crisis', 'while', 'short', 'lockdown', 'conditions', 'economic', 'instability', 'lower']
['stock', 'returns', 'stock', 'markets', 'witnessed', 'short']
['deficiency', 'health', 'management', 'systems', 'imperatively', 'damaged', 'stock', 'returns']
['london', 'stock', 'exchange', 'investigation', 'revealed', 'deficiency', 'health', 'systems']
['lockdown', 'conditions', 'imperatively', 'damaged', 'structure', 'financial', 'markets']
['inferring', 'sustainable', 'development', 'these', 'nations', 'covid']
['study', 'suggested', 'governments', 'should', 'allocate', 'their', 'budget', 'health']
['sector', 'overcome', 'health', 'crisis', 'future']
['keywords', 'covid', 'financial', 'stability', 'financial', 'markets', 'psychology', 'sustainable', 'development', 'global']
['development', 'health', 'crisis', 'economic', 'crisis']
['introduction']
['historically', 'countries', 'affected', 'pandemic', 'epidemic', 'large']
['impact', 'within', 'economy', 'their', 'financial', 'markets', 'specific']
['example', 'would', 'spread', 'ebola', 'disease', 'which', 'caused']
['billion', 'dollars', 'fernandes', 'however', 'potential', 'damages', 'current']
['virus', 'still', 'largely', 'unknown', 'significantly', 'coronavirus', 'covid', 'infection', 'disease']
['first', 'reported', 'wuhan', 'december', 'spread', 'rapidly', 'almost']
['whole', 'world', 'within', 'months', 'albulescu', 'compared', 'severe', 'acute']
['respiratory', 'syndrome', 'covid', 'contagious', 'which', 'indicated']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['different', 'fatality', 'rate1']
['covid', 'infected']
['individuals', 'deaths', 'documented']
['throughout', 'globe', 'until', 'april', 'financial', 'times']
['order', 'eradicate', 'pandemic', 'world', 'health']
['organization', 'recommended', 'maintaining', 'social', 'distance']
['which', 'generated', 'severe', 'lockdown', 'situation', 'globe']
['accordingly', 'economic', 'circle', 'whole']
['world', 'disturbed', 'notably', 'online', 'travel']
['agencies', 'airlines', 'hotels', 'unexpectedly', 'declined']
['world', 'economic', 'forum', 'prices', 'nosedived']
['sudden', 'outbreak', 'pandemic']
['globalization', 'current', 'coronavirus', 'outbreak', 'aggravate']
['economic', 'condition', 'which', 'toward', 'financial']
['meltdown', 'huang', 'pandemic', 'caused']
['severe', 'psychological', 'impact', 'economy', 'while', 'agitating']
['service', 'industries', 'financial', 'markets']
['moreover', 'coronavirus', 'outbreak', 'severely', 'affected']
['financial', 'markets', 'while', 'declining', 'value', 'stock', 'index']
['daube', 'surprisingly', 'specific', 'events']
['fluctuate', 'stock', 'values', 'shehzad', 'sohail', 'figure']
['revealed', 'market', 'value', 'european', 'american', 'chinese']
['markets', 'january', 'march']
['period', 'divided', 'three', 'rounds', 'comparison', 'purposes']
['first', 'lagged', 'january', 'january']
['second', 'lagged', 'january', 'march']
['third', 'lagged', 'denoted', 'period', 'march', 'march']
['results', 'showed', 'market', 'values', 'these']
['indices', 'significantly', 'declined', 'third', 'lagged', 'covid', 'approached', 'western', 'countries', 'spain', 'stock', 'market']
['share', 'value', 'decreased', 'third', 'lagged']
['covid', 'tremendously', 'shrank', 'greece', 'stock', 'markets']
['moreover', 'figure', 'illustrates', 'volatility', 'index']
['january', 'march', 'denotes', 'world', 'faced']
['severe', 'financial', 'crises', 'stock', 'markets', 'collapsed']
['economies', 'through', 'enormous', 'pressure', 'similar']
['scenario', 'built', 'during', 'global', 'pandemic', 'covid', 'stock', 'market', 'variance', 'highest', 'second']
['after', 'global', 'financial', 'crises']
['regard', 'novel', 'coronavirus', 'impact']
['detrimental', 'pandemic', 'behavior', 'unknown', 'effect']
['stock', 'return', 'longer', 'relevant']
['stock', 'volatility', 'agitated', 'enormous', 'spread']
['pandemic', 'resulting', 'severe', 'economic', 'crisis', 'sharif']
['circumstances', 'quite', 'significant']
['analyze', 'coronavirus', 'short', 'impacts']
['advanced', 'countries', 'stock', 'return', 'investigation', 'argued']
['confirmed', 'patients', 'novel', 'coronavirus', 'increase']
['lockdown', 'conditions', 'becomes', 'stricter', 'which']
['significant', 'economic', 'crisis', 'shocked', 'economic', 'stability']
['nations', 'moreover', 'deaths', 'befell', 'result', 'novel']
['coronavirus', 'specified', 'deficiency', 'health', 'facilities', 'which']
['caused', 'substantial', 'health', 'crisis', 'particularly', 'examination']
['analyzed', 'effects', 'economic', 'health', 'crisis']
['novel', 'coronavirus', 'behavior', 'financial', 'markets']
['fatality', 'while', 'fatality', 'novel', 'coronavirus']
['around', 'globe']
['united', 'states', 'germany', 'united', 'kingdom', 'italy']
['spain', 'period', 'february', 'april']
['study', 'elucidates', 'effects', 'executing', 'linear', 'autoregressive', 'distributed', 'lagged', 'nardl', 'model']
['investigation', 'provides', 'remarkable', 'policies', 'handle']
['impacts', 'covid', 'financial', 'market']
['answers', 'momentous', 'queries', 'researchers', 'policymakers']
['government', 'officials', 'academicians', 'firstly']
['covid', 'linear', 'impact', 'financial', 'markets']
['behavior', 'secondly', 'health', 'crisis', 'economic', 'instability']
['generated', 'covid', 'significant', 'impact']
['index', 'thirdly', 'nasdaq', 'composite', 'index']
['index', 'respond', 'critically', 'covid', 'crisis']
['fourthly', 'economic', 'crisis', 'generated', 'covid']
['health', 'crisis', 'significant', 'impact', 'stock', 'returns']
['fifthly', 'suspended', 'circle', 'economy']
['lockdown', 'begin', 'again', 'according', 'author', 'knowledge']
['first', 'study', 'examines', 'asymmetrical', 'impact']
['covid', 'psychology', 'stock', 'markets', 'mostly']
['infected', 'nations', 'world', 'firstly', 'project', 'ascertained']
['stationary', 'level', 'study', 'variables', 'discovered']
['variables', 'stationary', 'stationary']
['hence', 'linear', 'version', 'autoregressive']
['distributed', 'model', 'applied']
['methodology']
[]
['study', 'utilized', 'daily', 'number', 'confirmed']
['patients', 'deaths', 'covid', 'stock', 'markets']
['spain', 'italy', 'germany', 'period']
['february', 'april', 'study', 'taken']
['database', 'yahoo', 'finance', 'european', 'center']
['disease', 'control', 'prevention', 'further', 'investigation']
['analyzed', 'daily', 'returns']
['london', 'stock', 'exchange', 'nomination', 'spain', 'italy']
['germany', 'however', 'nasdaq', 'composite']
['index', 'represents']
['methodology']
['investigation', 'utilized', 'stock', 'market', 'daily', 'returns']
['dependent', 'variable', 'confirmed', 'cases', 'deaths']
['independent', 'variable', 'daily', 'returns', 'study']
['computed', 'follows', 'syllignakis', 'kouretas', 'shehzad']
['sohail']
[]
[]
[]
['denote', 'return', 'value', 'closing']
['price', 'previous', 'closing', 'price']
['stock', 'market', 'respectively', 'linear', 'association', 'between', 'these']
['variables', 'defined']
['1sprt', 'β1usct', 'β2usdt']
['1nsrt', 'β1usct', 'β2usdt']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['figure', 'deterioration', 'stock', 'markets', 'owing', 'novel', 'coronavirus']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['figure', 'variations', 'global', 'financial', 'crisis']
['covid', 'crisis']
['1ibert', 'β1spct', 'β2spdt']
['1ftmrt', 'β1itlct', 'β2itldt']
['1daxrt', 'β1gerct', 'β2gerdt']
['1lsert', 'β1ukct', 'β2ukdt']
['where', 'signifies', 'first', 'difference', 'β0β1β2']
['independent', 'parameters', 'however']
['defines', 'return', 'values', 'nasdaq']
['composite', 'index']
['london', 'stock', 'exchange', 'respectively', 'moreover']
['indicate', 'confirmed', 'cases', 'covid', 'spain', 'italy', 'germany', 'respectively']
['further', 'refer', 'confirmed']
['deaths', 'covid', 'spain', 'italy', 'germany']
['respectively']
['ascertain', 'short', 'asymmetries']
['study', 'employed', 'linear', 'autoregressive', 'distributed']
['nardl', 'model', 'introduced', 'model']
['performs', 'small', 'number', 'observations']
['applied', 'mixed', 'level', 'stationary']
['evaluates', 'linearity', 'cointegration', 'between', 'variables']
['equation', 'shared', 'short', 'variations']
['asymmetries', 'after', 'taking', 'parameters', 'account']
['nardl', 'model', 'extended']
['pesaran', 'model', 'hence']
['nardl', 'model', 'stock', 'market', 'specified']
['1sprt']
[]
[]
[]
['1sprt']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['γ2ausc']
['γ2busc']
['γ3ausd']
[]
[]
[]
['1nsrt']
[]
[]
[]
['1nsrt']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['γ1nsrt', 'γ2ausc']
['γ2busc']
[]
[]
['γ3busd']
[]
['1ibert']
[]
[]
[]
['1ibert']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['γ1ibert', 'γ2aspc']
['γ2bspc']
[]
[]
['γ3bspd']
[]
['1ftmrt']
[]
[]
[]
['1ftmrt']
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itld']
[]
[]
[]
[]
[]
[]
['γ1ftmrt', 'γ2aitlc']
[]
[]
[]
[]
[]
[]
[]
['1daxrt']
[]
[]
[]
['1daxrt']
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerc']
[]
[]
[]
[]
['1gerd']
[]
[]
[]
[]
[]
[]
['γ1daxrt', 'γ2agerc']
['γ2bgerc']
[]
[]
['γ3bgerd']
[]
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['1lsert']
[]
[]
[]
['1lsert']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['γ1lsert', 'γ2aukc']
['γ2bukc']
[]
[]
['γ3bukd']
[]
['ϑ2bϑ3aand', 'short', 'factors', 'while']
[]
[]
['indicates', 'parameters']
['whereas', 'symbolizes', 'number', 'model']
['based', 'akaik', 'information', 'criterion', 'schwarz', 'information']
['criterion', 'above', 'stated', 'equations', 'assumed']
['confirmed', 'number', 'cases', 'deaths', 'covid']
['asymmetrical', 'impact', 'stock', 'returns']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['designates', 'positive', 'shock', 'though']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['implies', 'negative', 'shock', 'variable']
['computed', 'follows']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['delineates', 'positive', 'shock', 'variable', 'denotes']
['asymmetric', 'distributive', 'error', 'correction']
['these', 'factors', 'written', 'follows']
['1sprt']
[]
[]
[]
['1sprt']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1nsrt']
[]
[]
[]
['1nsrt']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1ibert']
[]
[]
[]
['1ibert']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1ftmrt']
[]
[]
[]
['1ftmrt']
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itld']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1daxrt']
[]
[]
[]
['1daxrt']
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1lsert']
[]
[]
[]
['1lsert']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['φ1ectt']
['directs', 'error', 'correction']
['cointegration', 'among', 'variables', 'examined', 'through', 'bound']
['approach', 'pesaran', 'method', 'relies', 'evaluate', 'hypothesis']
['purpose', 'pesaran', 'defined', 'bounds']
['upper', 'lower', 'bound', 'estimated', 'statistics']
['higher', 'upper', 'bound', 'limit', 'hypothesis']
['cointegration', 'rejected', 'however', 'projected', 'value', 'lower', 'lower', 'bound', 'limit', 'hypothesis', 'cannot']
['rejected', 'besides', 'value', 'remains', 'between']
['limits', 'results', 'conclusive', 'project']
['employed', 'verify', 'asymmetric']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['andshort']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['asymmetric']
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['shehzad', 'financial', 'markets', 'behavior', 'covid']
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['significant', 'statistical', 'evidence', 'association', 'between']
['study', 'variables', 'equation', 'statistics', 'joint']
['significance', 'lagged', 'level', 'parameters', 'stated']
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['cointegration', 'exists', 'among', 'economic', 'crisis', 'health']
['crisis', 'financial', 'markets', 'equation']
['short', 'asymmetric']
['cointegration', 'reckoning']
['table', 'exhibited', 'upshots', 'statistics']
['particularized', 'linear', 'association']
['however', 'revealed', 'short', 'asymmetric']
['affiliation', 'besides', 'caused', 'asymmetric']
['influence', 'short', 'periods']
['while', 'possessed', 'short', 'asymmetry', 'additionally']
['table', 'descriptive', 'statistics']
[]
['41737', '08997', '70833', '28187']
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['kurtosis', '36907', '166164', '206693', '903565', '778953', '75376', '380136', '389965']
['jarque', '26244', '69524', '87966', '47298', '76708']
['probability', '00434', '000161']
[]
['18523', '54717', '42086', '40245']
['skewness', '927378', '53985', '120857', '230936', '65869', '232889', '058817', '31595']
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['probability', '000013', '003185', '000121', '000872']
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['table']
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['893713', '54426', '057616']
['293963', '969976', '971366', '51473']
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['shehzad', 'financial', 'markets', 'behavior', 'covid']
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[]
['statistic', '02410', 'statistic', '165881']
[]
[]
[]
['statistic', '11238', 'statistic', '23325']
[]
[]
[]
['statistic', '490181', 'statistic', '03259']
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[]
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[]
['242605']
[]
[]
['574046']
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[]
['569179', '725822', '640751']
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['nasdaq', 'composite', 'index']
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['table', 'diagnostic', 'results']
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[]
['statistic', 'statistic', 'statistic', 'statistic']
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[]
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[]
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[]
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['complex', 'emotional', 'responses', 'traumatic', 'events']
['characterize', 'language', 'people']
['suffering', 'mental', 'health', 'coppersmith']
['sadness']
['related', 'words', 'included', 'emolex']
['often', 'measure', 'tangible']
['intangible', 'threats']
['emolex']
['death']
['anxiety', 'panic', 'anxiety', 'category', 'study', 'ferent', 'forms', 'anxiety', 'social', 'media', 'rudzicz']
[]
['anxiety']
['disorientation', 'definition', 'tentative', 'category', 'expresses']
['uncertainty', 'tausczik', 'pennebaker']
['tentative']
['suspicion', 'suspicion', 'often', 'formalized', 'trust', 'deutsch']
[]
['trust']
['irrationality', 'negate', 'category', 'measure']
['cognitive', 'distorsions', 'irrational', 'interpretations', 'ality', 'simms']
['negate']
['religion', 'religious', 'expressions', 'study']
['people', 'appeal', 'religious', 'entities', 'during', 'moments']
['hardship']
['religion']
['contagion', 'these', 'categories', 'study']
['perception', 'diseases', 'cancer', 'support', 'groups', 'people']
['affected', 'eating', 'disorder', 'alcoholics', 'alpers']
['kornfield']
[]
[]
['moralization']
['avoidance']
['perception']
['category', 'model', 'perception', 'connected']
['epidemics']
[]
['polarization', 'segregation', 'different', 'personal', 'pronouns', 'study', 'group', 'dynamics', 'characterize', 'language']
['markers', 'racism', 'arguello', 'personal']
['pronouns', 'markers', 'differentiation']
['considered', 'studies', 'racist', 'language', 'figea']
[]
[]
[]
[]
['differ']
['stigmatization', 'blame']
['abuse']
['pronouns', 'quantify', 'blame']
['personal', 'borelli', 'sbarra', 'political', 'windsor', 'speech', 'associated']
['oriented', 'statements', 'elsherief']
['categories']
['previous']
['cooperation']
['coordination', 'collective']
['consciousness']
['moral', 'value', 'expresses', 'protecting']
['others', 'graham', 'cooperation', 'often']
['verbalized', 'referencing', 'group', 'expressing']
['affiliation', 'rezapour']
['affiliation']
['moral', 'virtue']
['prosocial', 'prosocial']
['faith', 'authority', 'moral', 'value', 'authority', 'expresses', 'playing']
['rules', 'hierarchy', 'versus', 'challenging', 'rezapour']
[]
['authority', 'moral']
['virtue']
[]
['authority', 'enforcement', 'power', 'category', 'expresses', 'exertion', 'inance', 'tausczik', 'pennebaker']
['power']
['action']
['restrictions', 'travel']
['motion']
[]
[]
['social']
['daily', 'habits', 'concern', 'mainly', 'people', 'experience']
['leisure', 'movement', 'between']
['gonzalez']
['leisure']
['table', 'operationalization', 'strong', 'epidemic', 'psychology', 'theoretical', 'framework', 'strong', 'paper']
['three', 'annotators', 'extracted', 'keywords', 'characterize', 'three', 'psycho', 'social', 'epidemics', 'mapped']
['relevant', 'language', 'categories', 'existing', 'language', 'lexicons', 'psychometric', 'studies', 'category', 'names']
['followed', 'their', 'corresponding', 'lexicon', 'parenthesis', 'support', 'association', 'between', 'keywords']
['language', 'categories', 'examples', 'supporting', 'literature', 'summarize', 'language', 'egories', 'varies', 'across', 'three', 'temporal', 'states', 'computed', 'values', 'different', 'language', 'categories']
['their', 'standardized', 'fractions', 'reached', 'maximum', 'reported', 'percentage', 'increase']
['compared', 'average', 'whole', 'period', 'maximum', 'value', 'highlighted', 'epidemic', 'psychology', 'works', 'twitter']
['constructed', 'temporal', 'signals', 'language', 'categories', 'counted', 'number', 'users', 'posted']
['least', 'tweet', 'contains', 'interaction', 'obtained', 'fraction', 'users', 'mentioned']
['interaction', 'dividing', 'total', 'number', 'users', 'tweeted']
[]
[]
[]
['normalized', 'these', 'fractions', 'considering', 'minimum', 'maximum', 'values', 'during', 'whole']
['period']
[]
[]
[]
['mentions', 'medical', 'entities', 'state', 'learning', 'method', 'medical', 'entity', 'extraction']
['identify', 'medical', 'symptoms', 'twitter', 'relation', 'covid', 'scepanovic', 'applied', 'tweets']
['method', 'extracts', 'grams', 'representing', 'medical', 'symptoms', 'feeling', 'method', 'based']
['sequence', 'tagging', 'architecture', 'huang', 'combination', 'glove', 'embeddings', 'nington', 'roberta', 'contextual', 'embeddings', 'optimize', 'entity', 'extraction']
['performance', 'noisy', 'textual', 'social', 'media', 'trained', 'sequence', 'tagging', 'architecture', 'micromed']
['database', 'jimeno', 'yepes', 'collection', 'tweets', 'manually', 'labeled', 'medical', 'entities', 'hyper', 'parameters', 'hidden', 'units', 'batch', 'learning', 'which', 'gradually', 'halved']
['whenever', 'there', 'performance', 'improvement', 'after', 'epochs', 'trained', 'maximum', 'epochs']
['before', 'learning', 'became', 'small', 'final', 'model', 'achieved', 'score', 'micromed']
['score', 'performance', 'measure', 'combines', 'precision', 'fraction', 'extracted', 'entities', 'actually']
['medical', 'entities', 'recall', 'fraction', 'medical', 'entities', 'present', 'method', 'retrieve']
['based', 'implementation', 'flair', 'akbik', 'pytorch', 'paszke', 'popular']
['learning', 'libraries', 'python']
['unique', 'medical', 'entity', 'counted', 'number', 'users', 'posted', 'least', 'tweet']
['mentioned', 'entity', 'obtained', 'fraction', 'users', 'mentioned', 'medical', 'entity', 'dividing']
['total', 'number', 'users', 'tweeted']
[]
[]
[]
['normalize', 'these', 'fractions', 'considering', 'minimum', 'maximum', 'values', 'during', 'whole']
['period']
[]
[]
[]
['mobility', 'traces', 'foursquare', 'local', 'search', 'discovery', 'mobile', 'application', 'relies', 'users', 'mobility']
['records', 'recommend', 'places', 'might', 'application', 'localization', 'estimate']
['position', 'infer', 'places', 'visited', 'response', 'covid', 'crisis', 'foursquare', 'publicly']
['available', 'gathered', 'million', 'users', 'these', 'users', 'always', 'during', 'period']
['collection', 'meaning', 'allowed', 'application', 'gather', 'location', 'times']
['application', 'published', 'through', 'visitdata', 'website', 'consists', 'daily', 'number']
['users', 'visiting', 'venue', 'state', 'starting', 'february', 'present']
['users', 'visited', 'schools', 'indiana', 'february', 'overall', 'distinct', 'location', 'categories', 'provided', 'obtain']
['country', 'temporal', 'indicators', 'first', 'applied', 'normalization', 'values']
[]
[]
[]
['averaged', 'values', 'across', 'states']
[]
[]
[]
[]
[]
['where', 'total', 'number', 'states', 'weighting', 'state', 'equally', 'obtained', 'measure']
['representative', 'whole', 'territory', 'rather', 'being', 'biased', 'towards', 'density', 'regions', 'epidemic', 'psychology', 'works', 'twitter']
['series', 'smoothing', 'temporal', 'indicators', 'affected', 'large', 'fluctuations', 'extract']
['consistent', 'trends', 'series', 'applied', 'smoothing', 'function', 'common', 'practice', 'analyzing']
['temporal', 'extracted', 'social', 'media', 'connor', 'given', 'varying', 'signal', 'apply']
['boxcar', 'moving', 'average', 'window', 'previous']
[]
[]
[]
[]
[]
[]
[]
[]
['selected', 'window', 'weekly', 'windows', 'typically', 'smooth']
['variations', 'weekly', 'periodicities', 'connor', 'applied', 'smoothing', 'series']
['language', 'categories']
[]
[]
['mentions', 'medical', 'entities']
[]
[]
['interaction', 'types']
[]
[]
[]
['foursquare', 'visits']
[]
[]
[]
['change', 'point', 'detection', 'identify', 'phases', 'characterized', 'different', 'combinations', 'language', 'categories']
['identified', 'change', 'points', 'periods', 'which', 'values', 'categories', 'varied', 'considerably', 'quantify']
['variations', 'language', 'category', 'computed']
[]
[]
['namely', 'daily', 'average', 'squared', 'gradi', 'utkepohl', 'smoothed', 'standardized', 'fractions', 'category', 'calculate', 'gradient']
['python', 'function', 'numpy', 'gradient', 'gradient', 'provides', 'measure', 'increase', 'decrease']
['signal', 'consider', 'absolute', 'value', 'gradient', 'account', 'magnitude', 'change', 'rather', 'direc', 'change', 'identify', 'periods', 'consistent', 'change', 'opposed', 'quick', 'instantaneous', 'shifts', 'apply', 'temporal']
['smoothing', 'equation', 'series', 'gradients', 'denote', 'smoothed', 'squared', 'gradients']
[]
['average', 'gradients', 'language', 'categories', 'obtain', 'overall', 'gradient']
[]
[]
[]
[]
[]
[]
[]
[]
[]
['peaks', 'series', 'represent', 'highest', 'variation', 'marked', 'change', 'points', 'using']
['python', 'function', 'scipy', 'signal', 'peaks', 'identified', 'peaks', 'local', 'maxima', 'whose', 'values', 'higher']
['average', 'standard', 'deviations', 'common', 'practice', 'palshikar']
['results']
['language', 'until', 'first', 'contagion', 'during', 'first', 'residents', 'experienced']
['pandemic', 'entailed', 'first', 'going', 'through', 'entire', 'cycle', 'making', 'contagion']
['contained', 'arrival', 'unknown', 'virus', 'skepticism', 'isolation', 'measures', 'first', 'reopening']
['figure', 'shows', 'standardized', 'fractions', 'language', 'categories', 'formula', 'changed']
['february', 'april', 'which', 'restrictions', 'states', 'lifted', 'color', 'encodes', 'values']
['higher', 'average', 'lower', 'partitioned', 'language', 'categories', 'according', 'three']
['psycho', 'social', 'epidemics', 'figure', 'shows', 'value', 'average', 'squared', 'gradient', 'formula']
['peaks', 'curve', 'represent', 'local', 'variation', 'marked', 'peaks', 'above', 'standard', 'deviations']
['change', 'points', 'found', 'change', 'points', 'coincide', 'events', 'february']
[]
['announcement', 'first', 'infection', 'country', 'march', 'announcement']
['orders', 'these', 'change', 'points', 'identify', 'three', 'phases', 'which', 'described', 'dwelling']
['peaks', 'different', 'language', 'categories', 'their', 'standardized', 'fractions', 'reached', 'maximum']
['reporting', 'percentage', 'increase', 'increase', 'compared', 'average', 'february', 'april']
[]
['denoted', 'table', 'first', 'phase', 'refusal', 'phase', 'characterized', 'anxiety']
['death', 'frequently', 'mentioned', 'february', 'compared', 'average', 'during']
['whole', 'period', 'pronoun', 'temporal', 'state', 'average', 'suggests']
['focus', 'discussion', 'implications', 'viral', 'epidemic', 'others', 'infection']
['discovered', 'other', 'language', 'categories', 'exhibited', 'significant', 'variations', 'which', 'reflected']
['overall', 'situation', 'business', 'usual']
['second', 'phase', 'anger', 'phase', 'began', 'february', 'outburst', 'negative', 'emotions', 'predominantly']
['anger', 'right', 'after', 'first', 'covid', 'contagion', 'announced', 'abstract', 'death', 'replaced']
['expressions', 'concrete', 'health', 'concerns', 'words', 'expressing', 'mentions', 'parts']
['march', 'federal', 'government', 'announced', 'state', 'national', 'emergency', 'followed', 'enforcement']
['state', 'level', 'orders', 'during', 'those', 'observed', 'sharp', 'increase', 'pronoun']
['swear', 'words', 'march', 'which', 'hints', 'climate', 'discussion', 'characterized']
['conflict', 'polarization', 'observed', 'increase', 'words', 'related', 'daily', 'habits']
['affected', 'impending', 'restriction', 'policies', 'motion', 'social', 'activities', 'leisure', 'mentions', 'words8', 'epidemic', 'psychology', 'works', 'twitter']
[]
['death']
['anxiety']
['negemo']
['anger']
['swear']
['tentative']
['negate']
[]
[]
['religion']
['posemo']
['sadness']
['trust']
[]
['refusal', 'phase', 'anger', 'phase', 'acceptance', 'phase']
[]
[]
[]
['differ']
[]
[]
['affiliation']
['authority']
['power']
[]
['prosocial']
['morality']
['focus', 'focus', 'focus']
[]
['motion']
[]
['social']
['leisure']
['action']
[]
['usual', 'beyond']
[]
[]
['gradient']
[]
[]
[]
[]
['custom', 'words']
[]
['alcohol', 'exercising', 'economic']
[]
[]
[]
['interactions']
[]
['support', 'power', 'conflict']
[]
[]
[]
['medical']
[]
['physical', 'health', 'mental', 'health']
[]
[]
[]
[]
['mobility']
[]
['first']
[]
[]
[]
['grocery', 'travel', 'transport', 'outdoors', 'recreation']
[]
[]
[]
[]
['figure', 'epidemic', 'psychology', 'twitter', 'during', 'first', 'contagion', 'evolution']
['different', 'language', 'categories', 'tweets', 'related', 'covid', 'heatmaps', 'represents']
['language', 'category', 'words', 'expressing', 'anxiety', 'manual', 'coding', 'associated', 'three']
['psycho', 'social', 'epidemics', 'color', 'represents', 'daily', 'standardized', 'fraction', 'people', 'words', 'related']
['category', 'values', 'higher', 'average', 'those', 'lower', 'categories']
['partitioned', 'three', 'groups', 'according', 'psycho', 'social', 'epidemics', 'model', 'morality', 'action']
['average', 'gradient', 'instantaneous', 'variation', 'language', 'categories', 'peaks', 'gradient', 'identify']
['change', 'points', 'dates', 'around', 'which', 'considerable', 'change', 'multiple', 'language', 'categories', 'happened']
['dashed', 'vertical', 'lines', 'cross', 'plots', 'represent', 'these', 'change', 'points', 'temporal', 'evolution']
['families', 'indicators', 'corroborate', 'validity', 'trends', 'identified', 'language', 'categories']
['checked', 'internal', 'validity', 'comparing', 'language', 'categories', 'custom', 'keyword', 'search', 'approach']
['learning', 'tools', 'extract', 'types', 'social', 'interactions', 'mentions', 'medical', 'symptoms']
['checked', 'external', 'validity', 'looking', 'mobility', 'patterns', 'different', 'venue', 'categories', 'estimated']
['localization', 'service', 'foursquare', 'mobile', 'timeline', 'bottom', 'figure', 'marks']
['events', 'covid', 'pandemic', 'announcements', 'first', 'infection', 'covid']
['recorded', 'epidemic', 'psychology', 'works', 'twitter']
['related', 'peaked', 'march', 'federal', 'government', 'announced', 'social', 'distancing']
['guidelines', 'place', 'least', 'weeks']
['third', 'phase', 'acceptance', 'phase', 'started', 'march', 'after', 'first', 'physical', 'distancing', 'measures']
['imposed', 'increased', 'words', 'power', 'authority', 'likely', 'reflected', 'emergence', 'discussion']
['around', 'policies', 'enforced', 'government', 'officials', 'public', 'agencies', 'death', 'raised', 'steadily']
['hitting', 'deaths', 'march', 'expressions', 'conflict', 'faded', 'words', 'sadness', 'became']
['predominant', 'those', 'hardship', 'sentiment', 'others', 'expressions', 'prosocial', 'behavior', 'became']
['frequent', 'respectively', 'mentions', 'related', 'activities', 'peaked', 'people']
['either', 'their', 'compelled', 'result', 'lockdown']
['thematic', 'analysis', 'language', 'categories', 'capture', 'broad', 'concepts', 'related', 'strong', 'epidemic', 'psychology']
['theory', 'allow', 'analysis', 'grained', 'topics', 'within', 'category', 'study']
['combinations', 'language', 'category', 'phase', 'language', 'categories', 'phases', 'listed']
['retweeted', 'tweets', 'popular', 'tweets', 'containing', 'anxiety', 'posted', 'refusal', 'phase']
['identify', 'overarching', 'themes', 'followed', 'steps', 'commonly', 'adopted', 'thematic', 'analysis', 'braun']
['clarke', 'smith', 'shinebourne', 'first', 'applied', 'coding', 'identify', 'concepts', 'emerged']
['across', 'multiple', 'tweets', 'specifically', 'authors', 'tweets', 'marked', 'keywords']
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['manner', 'rather', 'linear', 'evaluating', 'adjusting', 'tweets', 'parsed', 'table', 'summarizes']
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['skepticism', 'tweeted', 'widely', 'table', 'epidemic', 'frequently', 'depicted', 'foreign', 'problem']
['activities', 'business', 'usual']
['anger', 'phase', 'discussion', 'characterized', 'outrage', 'against', 'three', 'categories', 'foreigners', 'especially']
['chinese', 'individuals', 'supplementary', 'materials', 'detail', 'political', 'opponents', 'people', 'adopted']
['different', 'behavioral', 'responses', 'outbreak', 'level', 'conflict', 'corroborates', 'strong', 'postulate']
['against', 'other', 'science', 'religion', 'prominent', 'topics', 'discussion', 'lively', 'debate', 'raged']
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['science', 'mentions', 'people', 'isolating', 'became', 'frequent', 'highlighted', 'contrast']
['between', 'judicious', 'individuals', 'careless', 'crowds']
['finally', 'during', 'acceptance', 'phase', 'outburst', 'anger', 'sorrow', 'caused', 'mourning']
['thousands', 'people', 'accepting', 'threat', 'virus', 'twitter', 'users']
['collective', 'solutions', 'problem', 'overcome', 'although', 'positive', 'attitude', 'towards']
['authorities', 'seemed', 'prevalent', 'users', 'expressed', 'disappointment', 'against', 'restrictions', 'imposed']
['those', 'isolated', 'started', 'imagining', 'beyond', 'isolation', 'especially', 'relation', 'reopening']
['businesses']
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['results', 'output', 'alternative', 'mining', 'techniques', 'applied', 'internal', 'validity']
['world', 'mobility', 'traces', 'external', 'validity']
['comparison', 'other', 'mining', 'techniques', 'processed', 'social', 'media', 'posts', 'three', 'alternative']
['mining', 'techniques', 'figure', 'table', 'reported', 'three', 'language', 'categories', 'strongest']
['correlations', 'behavioral', 'marker']
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['economic', 'concerns', 'those', 'aspects', 'found', 'characterize', 'covid', 'pandemic', 'economist']
['measured', 'daily', 'fraction', 'users', 'mentioning', 'words', 'those', 'categories', 'figure']
['refusal', 'phase', 'frequency', 'these', 'words', 'significantly', 'increase', 'anger', 'phase', 'frequency']
['words', 'related', 'economy', 'peaked', 'related', 'alcohol', 'consumption', 'peaked', 'shortly', 'after', 'table']
['shows', 'economy', 'related', 'words', 'highly', 'correlated', 'anxiety', 'words', 'which']
['studies', 'indicating', 'degree', 'apprehension', 'declining', 'economy', 'comparable']
['health', 'hazard', 'concerns', 'fetzer', 'bareket', 'bojmel', 'words', 'alcohol', 'consumption']
['correlated', 'language', 'dimensions', 'period']
['health', 'concerns', 'their', 'isolation', 'caused', 'rising', 'alcohol', 'finlay']
['gilmore', 'finally', 'acceptance', 'phase', 'frequency', 'words', 'related', 'physical', 'exercise', 'significant', 'epidemic', 'psychology', 'works', 'twitter']
['theme', 'example', 'tweets']
['refusal', 'phase']
['denial', 'cases', 'result', 'death', 'approximately', 'equivalent', 'seasonal', 'relax']
['people']
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['business', 'usual', 'agriculture', 'specialists', 'dulles', 'airport', 'continue', 'protect', 'nation', 'vital', 'agricultural']
['resources']
['anger', 'phase']
['anger', 'foreign']
['there', 'anything', 'hatred', 'against', 'foreigner', 'covid19']
['global', 'pandemic']
['anger', 'political']
['opponents']
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['grotesque', 'about', 'cheering', 'coronavirus', 'street', 'crashing', 'because']
['father', 'winning', 'streak']
['anger']
['other']
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['healthcare', 'professionals', 'fauci', 'others', 'political', 'punditry']
['religion', 'problem', 'handle', 'overcome', 'threat']
['focus', 'people', 'upset', 'annoyed', 'tweet', 'about', 'coronavirus', 'people']
['avoid', 'crowds', 'category', 'coronavirus']
['favor', 'others']
['acceptance', 'phase']
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['number', 'lives', 'forever']
['focus', 'thankful', 'japan', 'friendship', 'cooperation', 'stand', 'together', 'defeat']
['covid19', 'pandemic', 'during', 'tough', 'times', 'friends', 'stick', 'together']
['thankful', 'taiwan', 'donating', 'million', 'masks', 'support', 'healthcare']
['choose', 'covid', 'overcome']
['together']
['authority', 'church', 'seeds', 'paint', 'operate', 'business', 'beach']
['masks', 'public']
['taxes', 'hopefully', 'celebrate', 'freedom']
['resuming', 'working', 'families', 'small', 'businesses', 'possible', 'workers']
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['happened', 'positive', 'words', 'expressing', 'togetherness', 'highest', 'affiliation']
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['approaches', 'matching', 'epidemic', 'psychology', 'works', 'twitter']
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['acceptance', 'affiliation', 'prosocial', 'anxiety', 'swear']
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['families', 'symptoms', 'respectively', 'those', 'related', 'physical', 'health', 'fever', 'cough', 'those', 'related']
['mental', 'health', 'depression', 'stress', 'brooks', 'normalized', 'fractions', 'users']
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['mentioned', 'correlated', 'language', 'categories', 'expressing', 'panic', 'physical', 'health', 'concerns']
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[]
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[]
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[]
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[]
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[]
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[]
['ferrara', 'varol', 'davis', 'menczer', 'flammini', 'social', 'communications']
['epidemic', 'psychology', 'works', 'twitter']
['fetzer', 'hensel', 'hermle', 'coronavirus', 'perceptions', 'economic', 'anxiety', 'review', 'economics']
['statistics', 'pages']
['figea', 'kaati', 'scrivens', 'measuring', 'online', 'affects', 'white', 'supremacy', 'forum', 'conference']
['intelligence', 'security', 'informatics', 'pages']
['finlay', 'gilmore', 'covid', 'alcohol', 'dangerous', 'cocktail']
['fiske', 'cuddy', 'glick', 'universal', 'dimensions', 'social', 'cognition', 'warmth', 'competence', 'trends']
['cognitive', 'sciences']
['frimer', 'schaefer', 'oakes', 'moral', 'actor', 'selfish', 'agent', 'journal', 'personality', 'social', 'psychology']
[]
['liang', 'saroha', 'people', 'react', 'virus', 'outbreaks']
['twitter', 'computational', 'content', 'analysis', 'american', 'journal', 'infection', 'control']
['galea', 'merchant', 'lurie', 'mental', 'health', 'consequences', 'covid', 'physical', 'distancing']
['prevention', 'early', 'intervention', 'internal', 'medicine']
['liang', 'kruse', 'mapping', 'county', 'level', 'mobility', 'pattern', 'changes', 'united']
['states', 'response', 'covid', 'sigspatial', 'special']
['gibbs', 'thematic', 'coding', 'categorizing', 'analyzing', 'qualitative', 'london', 'pages']
['french', 'gergle', 'oberlander', 'language', 'emotion', 'short', 'texts', 'proceedings']
['conference', 'computer', 'supported', 'cooperative', 'pages']
['goffman', 'stigma', 'notes', 'management', 'spoiled', 'identity', 'simon', 'schuster']
['gonzalez', 'hidalgo', 'barabasi', 'understanding', 'individual', 'human', 'mobility', 'patterns', 'nature']
[]
['graham', 'haidt', 'nosek', 'liberals', 'conservatives', 'different', 'moral', 'foundations']
['journal', 'personality', 'social', 'psychology']
['graham', 'haidt', 'koleva', 'motyl', 'wojcik', 'ditto', 'moral', 'foundations', 'theory']
['pragmatic', 'validity', 'moral', 'pluralism', 'advances', 'experimental', 'social', 'psychology', 'volume', 'pages']
['elsevier']
['grossman', 'rexer', 'thirumurthy', 'political', 'partisanship', 'influences', 'behavioral', 'responses']
['governors', 'recommendations', 'covid', 'prevention', 'united', 'states', 'available', '3578695']
['hebert', 'clemow', 'pbert', 'ockene', 'ockene', 'social', 'desirability', 'dietary', 'report']
['compromise', 'validity', 'dietary', 'intake', 'measures', 'international', 'journal', 'epidemiology']
['hochreiter', 'schmidhuber', 'short', 'memory', 'neural', 'computation']
['hofstede', 'hofstede', 'minkov', 'cultures', 'organizations', 'software', 'volume']
['mcgraw']
['holmes', 'connor', 'perry', 'tracey', 'wessely', 'arseneault', 'ballard', 'christensen']
['silver', 'everall', 'multidisciplinary', 'research', 'priorities', 'covid', 'pandemic', 'action', 'mental']
['health', 'science', 'lancet', 'psychiatry']
['jiang', 'assessment', 'public', 'attention', 'perception', 'emotional']
['behavioural', 'responses', 'covid', 'outbreak', 'social', 'media', 'surveillance', 'china', 'perception', 'emotional']
['behavioural', 'responses', 'covid', 'outbreak', 'social', 'media', 'surveillance', 'china']
['huang', 'bidirectional', 'models', 'sequence', 'tagging', 'arxiv', 'preprint', 'arxiv', '01991']
[]
['auriemma', 'cashaw', 'report', 'underreporting', 'depression', 'journal']
['personality', 'assessment', 'epidemic', 'psychology', 'works', 'twitter']
['husnayain', 'applications', 'google', 'search', 'trends', 'communication', 'infectious']
['disease', 'management', 'study', 'covid', 'outbreak', 'taiwan', 'international', 'journal', 'infectious', 'diseases']
[]
['jimeno', 'yepes', 'mackinlay', 'identifying', 'diseases', 'drugs', 'symptoms', 'twitter']
['studies', 'health', 'technology', 'informatics']
['johnson', 'fendrich', 'modeling', 'sources', 'report', 'survey', 'epidemiology', 'annals']
['epidemiology']
['tobin', 'massey', 'anderson', 'measuring', 'emotional', 'expression', 'linguistic']
['inquiry', 'count', 'american', 'journal', 'psychology', 'pages']
['huremovi', 'psychology', 'pandemic', 'psychiatry', 'pandemics', 'pages', 'springer']
['kornfield', 'gustafson', 'before', 'relapse']
['language', 'online', 'discussion', 'forum', 'predicts', 'risky', 'drinking', 'among', 'those', 'recovery', 'health']
['communication']
['kouzy', 'jaoude', 'kraitem', 'karam', 'zarka', 'traboulsi']
['baddour', 'coronavirus', 'viral', 'quantifying', 'covid', 'misinformation', 'epidemic', 'twitter', 'cureus']
[]
['ubler', 'wessler', 'avioli', 'death', 'dying']
['goodchild', 'spatial', 'temporal', 'socioeconomic', 'patterns', 'twitter', 'flickr']
['cartography', 'geographic', 'information', 'science']
['impact', 'covid', 'epidemic', 'declaration', 'psychological']
['consequences', 'study', 'active', 'weibo', 'users', 'international', 'journal', 'environmental', 'research', 'public', 'health']
[]
['liang', 'effect', 'covid', 'youth', 'mental', 'health']
['psychiatric', 'quarterly', 'pages']
['goyal', 'joshi', 'lewis', 'zettlemoyer', 'stoyanov', 'roberta']
['robustly', 'optimized', 'pretraining', 'approach', 'arxiv', 'preprint', 'arxiv', '11692']
['utkepohl', 'introduction', 'multiple', 'series', 'analysis', 'springer', 'science', 'business', 'media']
['maslow', 'theory', 'human', 'motivation', 'psychological', 'review']
['mcconnell', 'banks', 'avian', 'planning', 'possible', 'pandemic', 'trading', 'technology']
['mohammad', 'turney', 'crowdsourcing', 'emotion', 'association', 'lexicon', 'computational', 'intelligence']
[]
['moinet', 'pastor', 'satorras', 'barrat', 'effect', 'perception', 'epidemic', 'spreading', 'temporal', 'networks']
['physical', 'review', '012313']
['connor', 'balasubramanyan', 'routledge', 'smith', 'tweets', 'polls', 'linking', 'sentiment']
['public', 'opinion', 'series', 'fourth', 'international', 'conference', 'weblogs', 'social', 'media']
['oyeyemi', 'gabarron', 'ebola', 'twitter', 'misinformation', 'dangerous', 'combination']
['g6178']
['painter', 'political', 'beliefs', 'affect', 'compliance', 'covid', 'social', 'distancing', 'orders', 'available']
['3569098']
['palshikar', 'simple', 'algorithms', 'detection', 'series', 'advanced']
['analysis', 'business', 'analytics', 'intelligence', 'volume']
['paszke', 'gross', 'chintala', 'chanan', 'devito', 'desmaison', 'antiga', 'lerer']
['automatic', 'differentiation', 'pytorch', 'proceedings', 'advances', 'neural', 'information', 'processing', 'systems']
['autodiff', 'workshop', 'epidemic', 'psychology', 'works', 'twitter']
['pennington', 'socher', 'manning', 'glove', 'global', 'vectors', 'representation', 'proceedings']
['conference', 'empirical', 'methods', 'natural', 'language', 'processing', 'pages', 'association', 'computational']
['linguistics']
['perrin', 'anderson', 'share', 'adults', 'using', 'social', 'media', 'including', 'facebook']
['mostly', 'unchanged', 'since', 'https', 'pewresearch']
['share', 'adults', 'using', 'social', 'media', 'including', 'facebook', 'mostly', 'unchanged', 'since']
['plutchik', 'emotions', 'university', 'press', 'america']
['pulido', 'villarejo', 'carballido', 'redondo', 'covid', 'infodemic', 'retweets']
['science', 'based', 'information', 'coronavirus', 'false', 'information', 'international', 'sociology']
['0268580920914755']
['nationwide', 'survey', 'psychological', 'distress', 'among', 'chinese']
['people', 'covid', 'epidemic', 'implications', 'policy', 'recommendations', 'general', 'psychiatry']
['rezapour', 'diesner', 'enhancing', 'measurement', 'social', 'effects', 'capturing', 'morality']
['proceedings', 'tenth', 'workshop', 'computational', 'approaches', 'subjectivity', 'sentiment', 'social', 'media']
['analysis', 'pages']
['russell', 'mining', 'social', 'mining', 'facebook', 'twitter', 'linkedin', 'google', 'github']
['reilly', 'media']
['alani', 'alleviating', 'sparsity', 'twitter', 'sentiment', 'analysis', 'workshop', 'proceedings']
[]
['salathe', 'bengtsson', 'bodnar', 'brewer', 'brownstein', 'buckee', 'campbell', 'cattuto']
['khandelwal', 'mabry', 'digital', 'epidemiology', 'comput', 'e1002616']
['scepanovic', 'martin', 'lopez', 'quercia', 'baykaner', 'extracting', 'medical', 'entities', 'social', 'media']
['proceedings', 'conference', 'health', 'inference', 'learning', 'pages']
['schutz', 'luckmann', 'zaner', 'engelhardt', 'structures', 'world', 'number', 'northwest', 'university', 'studies', 'phenomenology', 'existential', 'philosophy', 'northwestern', 'university', 'press']
['9780810106222', 'https', 'books', 'google', 'books', 'lgxbxi0xsh8c']
['gustafson', 'hawkins', 'cleary', 'mctavish', 'pingree', 'eliason']
['lumpkins', 'effects', 'prayer', 'religious', 'expression', 'within', 'computer', 'support', 'groups', 'women', 'breast']
['cancer', 'psycho', 'oncology', 'journal', 'psychological', 'social', 'behavioral', 'dimensions', 'cancer']
[]
['rudzicz', 'detecting', 'anxiety', 'through', 'reddit', 'proceedings', 'fourth', 'workshop', 'compu', 'tational', 'linguistics', 'clinical', 'psychology', 'linguistic', 'signal', 'clinical', 'reality', 'pages']
['shultz', 'baingana', 'neria', 'ebola', 'outbreak', 'mental', 'health', 'current', 'status', 'recom', 'mended', 'response']
['simms', 'ramstedt', 'richards', 'martinez', 'giraud', 'carrier', 'detecting', 'cognitive', 'distortions']
['through', 'machine', 'learning', 'analytics', 'international', 'conference', 'healthcare', 'informatics']
['pages']
['smith', 'shinebourne', 'interpretative', 'phenomenological', 'analysis', 'american', 'psychological', 'association']
[]
['sommariva', 'vamos', 'mantzarlis', 'martinez', 'tyson', 'spreading', 'exploring']
['health', 'messages', 'social', 'media', 'implications', 'health', 'professionals', 'using', 'study', 'american', 'journal']
['health', 'education']
['strong', 'epidemic', 'psychology', 'model', 'sociology', 'health', 'illness']
['tajfel', 'turner', 'austin', 'worchel', 'integrative', 'theory', 'intergroup', 'conflict', 'organizational']
['identity']
['tausczik', 'pennebaker', 'psychological', 'meaning', 'words', 'computerized', 'analysis']
['methods', 'journal', 'language', 'social', 'psychology', 'epidemic', 'psychology', 'works', 'twitter']
['economist', 'millions', 'stuck', 'online', 'wellness', 'dustry', 'booming', 'https', 'economist', 'international']
['millions', 'stuck', 'online', 'wellness', 'industry', 'booming']
['bavel', 'baicker', 'boggio', 'capraro', 'cichocka', 'cikara', 'crockett']
['douglas', 'druckman', 'using', 'social', 'behavioural', 'science', 'support', 'covid', 'pandemic', 'response']
['nature', 'human', 'behaviour', 'pages']
['varol', 'ferrara', 'davis', 'menczer', 'flammini', 'online', 'human', 'interactions', 'detection']
['estimation', 'characterization', 'eleventh', 'international', 'conference', 'social', 'media']
['immediate', 'psychological', 'responses', 'associated']
['factors', 'during', 'initial', 'stage', 'coronavirus', 'disease', 'covid', 'epidemic', 'among', 'general', 'population']
['china', 'international', 'journal', 'environmental', 'research', 'public', 'health']
['waterloo', 'baumgartner', 'peter', 'valkenburg', 'norms', 'online', 'expressions', 'emotion']
['comparing', 'facebook', 'twitter', 'instagram', 'whatsapp', 'media', 'society']
['windsor', 'dowell', 'graesser', 'language', 'autocrats', 'leaders', 'language', 'natural', 'disaster', 'crises']
['hazards', 'crisis', 'public', 'policy']
['wojcik', 'hughes', 'sizing', 'twitter', 'users', 'washington', 'research', 'center']
['theis', 'kordy', 'language', 'eating', 'disorder', 'blogs', 'psychological', 'implications', 'social', 'online']
['activity', 'journal', 'language', 'social', 'psychology']
['propagating', 'debunking', 'conspiracy', 'theories', 'twitter', 'during', 'virus', 'outbreak']
['cyberpsychology', 'behavior', 'social', 'networking']
['torres', 'menczer', 'prevalence', 'credibility', 'information', 'twitter', 'during']
['covid', 'outbreak', 'arxiv', 'preprint', 'arxiv', '14484']
['acknowledgments']
['thank', 'sarah', 'konrath', 'rosta', 'farzan', 'licia', 'capra', 'their', 'useful', 'feedback', 'manuscript', 'research']
['partly', 'supported', 'grant', 'green', 'routes', '869764']
['availability']
['daily', 'aggregates', 'measurements', 'available', 'https', 'figshare', '14892642']
['tweet', 'available', 'https', 'github', 'echen102', 'covid', 'tweetids', 'other', 'datasets']
['visualizations', 'available', 'project', 'social', 'dynamics', 'epidemicpsychology']
['competing', 'interests']
['author', 'declare', 'competing', 'interests', 'epidemic', 'psychology', 'works', 'twitter']
['supplementary', 'materials']
['spatial', 'representativeness', 'twitter', 'geographical', 'penetration', 'twitter']
['reflects', 'spatial', 'distribution', 'population', 'compared', 'activity', 'state']
['census', 'population', 'figure', 'shows', 'number', 'twitter', 'users', 'volume', 'their', 'tweets']
['correlate', 'strongly', 'census', 'population', 'population', 'estimates', 'level', 'states']
['https', 'census']
[]
['population']
[]
[]
[]
[]
[]
['tweets']
[]
[]
[]
[]
[]
['population']
[]
[]
[]
[]
[]
[]
['users']
[]
[]
[]
[]
['figure', 'correlation', 'census', 'population', 'states', 'estimated']
['number', 'tweets', 'posted', 'those', 'states', 'number', 'unique', 'twitter', 'users', 'posted']
['least', 'tweet', 'right', 'during', 'period', 'analysis', 'linear', 'shown', 'together']
['coefficient', 'determination', 'linear', 'regression']
['pearson', 'correlation', 'coefficient']
['coefficient', 'number', 'tweets', 'twitter', 'users', 'scale', 'almost', 'linearly']
['population', 'estimates']
['signals', 'racism', 'twitter', 'discussions', 'corroborate', 'qualitative', 'intuition', 'first', 'phase']
['characterized', 'discussions', 'depicted', 'pandemic', 'foreign', 'problem', 'often', 'using', 'racist', 'undertones']
['measured', 'volume', 'tweets', 'containing', 'hashtags', 'clear', 'indicators', 'content', 'either', 'racist']
['aimed', 'antagonizing', 'china', 'manually', 'selected', 'those', 'hashtags', 'hashtags', 'appeared']
['least', 'times', 'dataset', 'figure', 'shows', 'normalized', 'volume', 'those', 'hashtags']
['during', 'first', 'phase', 'related', 'mentions', 'focus', 'twitter', 'discussions', 'china', 'asian']
['foreigners', 'their', 'peaks']
['breakdown', 'phases', 'state', 'during', 'period', 'analysis', 'country', 'fragmented', 'regions']
['dealing', 'different', 'stages', 'outbreak', 'policies', 'contrast', 'varied', 'across', 'states', 'assess']
['whether', 'those', 'differences', 'impacted', 'unfolding', 'psycho', 'social', 'epidemics', 'across', 'states', 'computed']
['measures', 'represent', 'relative', 'presence', 'three', 'phases', 'refusal', 'anger', 'acceptance']
['state', 'individually', 'results', 'shown', 'figure', 'despite', 'minor', 'differences', 'state', 'level', 'curves']
['indicate', 'three', 'phases', 'place', 'states', 'sequence', 'phases', 'consistent', 'across', 'states']
['refusal', 'followed', 'anger', 'which', 'followed', 'acceptance', 'there', 'little', 'variation']
['times', 'transition', 'between', 'phases', 'epidemic', 'psychology', 'works', 'twitter']

# LSA Model
number_of_topics=20
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_psy_paragraph=create_gensim_lsa_model(clean_text,number_of_topics,words)

dict(model_psy.show_topic(0, topn=5))

{'covid': 0.4453016891852977,
 'pandem': 0.3598206556230589,
 'health': 0.3346202937615597,
 'polici': 0.26271950834574903,
 'impact': 0.21515356479094486}

from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph29.png')

# LSA Model
a=pd.DataFrame()

for i in range(number_of_topics):
    words_psychology =dict(model_psy.show_topic(i, topn=words))
    b=pd.DataFrame(words_psychology,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled14.png')

cosine-similarity
              covid       pandem      health      polici      impact
covid             1  0.000945069   -0.010216  0.00629539    0.039352
pandem  0.000945069            1 -0.00824093   0.0267821 -0.00426252
health    -0.010216  -0.00824093           1   0.0445099  -0.0354746
polici   0.00629539    0.0267821   0.0445099           1   0.0298569
impact     0.039352  -0.00426252  -0.0354746   0.0298569           1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *100  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig14.png')

mantel_test(5)

Pairwise-distance
            covid     pandem    health     polici       impact      imag  \
covid           0   0.958406     1.002   0.988247     0.985564   1.39644   
pandem   0.958406          0  0.150457  0.0473991    0.0451842  0.988948   
health      1.002   0.150457         0   0.165412     0.166753   1.00689   
polici   0.988247  0.0473991  0.165412          0     0.016343  0.983969   
impact   0.985564  0.0451842  0.166753   0.016343  4.65661e-10   0.98335   
imag      1.39644   0.988948   1.00689   0.983969      0.98335         0   
detect    1.29896   0.869252  0.883351   0.848802     0.853976   1.30243   
model     1.39867   0.989329   1.00854   0.987349     0.987668   1.39232   
dataset   1.39968   0.996124   1.00287   0.986308     0.986155   1.39618   

           detect        model      dataset  
covid     1.29896      1.39867      1.39968  
pandem   0.869252     0.989329     0.996124  
health   0.883351      1.00854      1.00287  
polici   0.848802     0.987349     0.986308  
impact   0.853976     0.987668     0.986155  
imag      1.30243      1.39232      1.39618  
detect          0      1.29656       1.3039  
model     1.29656  1.49012e-08       1.3952  
dataset    1.3039       1.3952  1.49012e-08  
Pairwise-distance
               covid    pandem       health    polici    impact       imag  \
covid    1.49012e-08   1.39371       1.3671   1.09956   1.15352   0.988788   
pandem       1.39371         0      1.36229   1.08627     1.173   0.984106   
health        1.3671   1.36229  1.49012e-08   1.03485   1.14827    0.93486   
polici       1.09956   1.08627      1.03485         0  0.789259   0.488335   
impact       1.15352     1.173      1.14827  0.789259         0   0.634201   
imag        0.988788  0.984106      0.93486  0.488335  0.634201          0   
detect      0.988799  0.984939     0.935991    0.4889  0.638695  0.0212784   
model        1.02773   1.01363     0.971414  0.593183  0.551071   0.238385   
dataset      0.98695  0.984041      0.93458  0.488377  0.636966  0.0137386   

            detect     model    dataset  
covid     0.988799   1.02773    0.98695  
pandem    0.984939   1.01363   0.984041  
health    0.935991  0.971414    0.93458  
polici      0.4889  0.593183   0.488377  
impact    0.638695  0.551071   0.636966  
imag     0.0212784  0.238385  0.0137386  
detect           0  0.242376  0.0135129  
model     0.242376         0   0.240337  
dataset  0.0135129  0.240337          0  

(-0.47233691706581515, 0.93007, -1.4352624419113937)

mantel_test(10)

Pairwise-distance
              covid     pandem    health     polici       impact    countri  \
covid             0   0.958406     1.002   0.988247     0.985564   0.986266   
pandem     0.958406          0  0.150457  0.0473991    0.0451842  0.0742094   
health        1.002   0.150457         0   0.165412     0.166753   0.182912   
polici     0.988247  0.0473991  0.165412          0     0.016343  0.0645547   
impact     0.985564  0.0451842  0.166753   0.016343  4.65661e-10  0.0691747   
countri    0.986266  0.0742094  0.182912  0.0645547    0.0691747          0   
develop    0.981158  0.0478543  0.161234   0.033688    0.0369141  0.0688784   
nation     0.992413  0.0448254  0.154826  0.0228802    0.0232452  0.0631118   
psycholog  0.990363  0.0603611  0.185693  0.0216142    0.0247095  0.0676272   
sustain    0.990347  0.0612376  0.186512  0.0226199    0.0256283  0.0682887   
imag        1.39644   0.988948   1.00689   0.983969      0.98335   0.988474   
detect      1.29896   0.869252  0.883351   0.848802     0.853976   0.849716   
model       1.39867   0.989329   1.00854   0.987349     0.987668   0.995089   
dataset     1.39968   0.996124   1.00287   0.986308     0.986155   0.987376   
learn       1.37238   0.940281  0.912263    0.94437     0.933525    0.95111   
pneumonia   1.26607    0.80644  0.821003   0.790159      0.78868   0.806069   
use         1.38383   0.959732  0.962294   0.958242     0.962738   0.968914   
class       1.37578   0.963683  0.974078   0.955794      0.95556   0.964711   
chest      0.985739   0.190078  0.237957   0.176691     0.181627   0.197774   

             develop     nation   psycholog      sustain      imag    detect  \
covid       0.981158   0.992413    0.990363     0.990347   1.39644   1.29896   
pandem     0.0478543  0.0448254   0.0603611    0.0612376  0.988948  0.869252   
health      0.161234   0.154826    0.185693     0.186512   1.00689  0.883351   
polici      0.033688  0.0228802   0.0216142    0.0226199  0.983969  0.848802   
impact     0.0369141  0.0232452   0.0247095    0.0256283   0.98335  0.853976   
countri    0.0688784  0.0631118   0.0676272    0.0682887  0.988474  0.849716   
develop            0  0.0373929   0.0452732    0.0465989  0.980472  0.852867   
nation     0.0373929          0   0.0390727    0.0401159  0.983432  0.853328   
psycholog  0.0452732  0.0390727           0   0.00207226  0.981748  0.846731   
sustain    0.0465989  0.0401159  0.00207226  3.63798e-12  0.981729  0.846469   
imag        0.980472   0.983432    0.981748     0.981729         0   1.30243   
detect      0.852867   0.853328    0.846731     0.846469   1.30243         0   
model       0.973088   0.989249    0.986478     0.986496   1.39232   1.29656   
dataset     0.981582   0.986905    0.986962     0.986761   1.39618    1.3039   
learn       0.941637   0.941492    0.945452     0.945334   1.35922   1.30405   
pneumonia   0.797802   0.792535    0.790023     0.789838   1.26861   1.13873   
use         0.947366   0.965356    0.961157      0.96093   1.38249   1.30539   
class       0.960884   0.955751    0.956388     0.956518   1.37136   1.28796   
chest       0.170227   0.184296    0.179037     0.179021  0.882261  0.783617   

                 model      dataset     learn pneumonia       use     class  \
covid          1.39867      1.39968   1.37238   1.26607   1.38383   1.37578   
pandem        0.989329     0.996124  0.940281   0.80644  0.959732  0.963683   
health         1.00854      1.00287  0.912263  0.821003  0.962294  0.974078   
polici        0.987349     0.986308   0.94437  0.790159  0.958242  0.955794   
impact        0.987668     0.986155  0.933525   0.78868  0.962738   0.95556   
countri       0.995089     0.987376   0.95111  0.806069  0.968914  0.964711   
develop       0.973088     0.981582  0.941637  0.797802  0.947366  0.960884   
nation        0.989249     0.986905  0.941492  0.792535  0.965356  0.955751   
psycholog     0.986478     0.986962  0.945452  0.790023  0.961157  0.956388   
sustain       0.986496     0.986761  0.945334  0.789838   0.96093  0.956518   
imag           1.39232      1.39618   1.35922   1.26861   1.38249   1.37136   
detect         1.29656       1.3039   1.30405   1.13873   1.30539   1.28796   
model      1.49012e-08       1.3952   1.36274   1.26832   1.37644   1.37834   
dataset         1.3952  1.49012e-08   1.36981   1.25933   1.38163   1.37785   
learn          1.36274      1.36981         0    1.2345   1.36296   1.35307   
pneumonia      1.26832      1.25933    1.2345         0   1.23553   1.23926   
use            1.37644      1.38163   1.36296   1.23553         0   1.36188   
class          1.37834      1.37785   1.35307   1.23926   1.36188         0   
chest          1.00431     0.973249  0.970311  0.811805  0.899522   1.00454   

              chest  
covid      0.985739  
pandem     0.190078  
health     0.237957  
polici     0.176691  
impact     0.181627  
countri    0.197774  
develop    0.170227  
nation     0.184296  
psycholog  0.179037  
sustain    0.179021  
imag       0.882261  
detect     0.783617  
model       1.00431  
dataset    0.973249  
learn      0.970311  
pneumonia  0.811805  
use        0.899522  
class       1.00454  
chest             0  
Pairwise-distance
                 covid    pandem       health    polici    impact   countri  \
covid      1.49012e-08   1.39371       1.3671   1.09956   1.15352   1.29559   
pandem         1.39371         0      1.36229   1.08627     1.173   1.31319   
health          1.3671   1.36229  1.49012e-08   1.03485   1.14827   1.26177   
polici         1.09956   1.08627      1.03485         0  0.789259  0.921808   
impact         1.15352     1.173      1.14827  0.789259         0   1.08576   
countri        1.29559   1.31319      1.26177  0.921808   1.08576         0   
develop        1.03264   1.05574     0.984925  0.207274   0.65402   0.82199   
nation         1.00858   1.01158      0.95083  0.271159  0.611488  0.847083   
psycholog      1.38935   1.38506      1.35027   1.09686   1.10595   1.28586   
sustain        1.00589   1.00368     0.961561  0.299541  0.619232  0.875245   
imag          0.988788  0.984106      0.93486  0.488335  0.634201  0.839667   
detect        0.988799  0.984939     0.935991    0.4889  0.638695  0.836366   
model          1.02773   1.01363     0.971414  0.593183  0.551071  0.867328   
dataset        0.98695  0.984041      0.93458  0.488377  0.636966  0.840011   
learn         0.990224  0.977253     0.933886  0.454435  0.635038   0.83892   
pneumonia     0.989015  0.983459     0.934593  0.488653  0.634424  0.838624   
use           0.989909   0.98432     0.935058  0.497721  0.632756  0.835079   
class         0.989045  0.984206     0.933089  0.488437  0.636346  0.839838   
chest                0         0            0         0         0         0   

            develop       nation psycholog   sustain        imag     detect  \
covid       1.03264      1.00858   1.38935   1.00589    0.988788   0.988799   
pandem      1.05574      1.01158   1.38506   1.00368    0.984106   0.984939   
health     0.984925      0.95083   1.35027  0.961561     0.93486   0.935991   
polici     0.207274     0.271159   1.09686  0.299541    0.488335     0.4889   
impact      0.65402     0.611488   1.10595  0.619232    0.634201   0.638695   
countri     0.82199     0.847083   1.28586  0.875245    0.839667   0.836366   
develop           0     0.106403   1.01246  0.159172    0.314653   0.315289   
nation     0.106403  6.45239e-09   1.00649  0.113097    0.234498   0.235503   
psycholog   1.01246      1.00649         0  0.933825    0.970727   0.969044   
sustain    0.159172     0.113097  0.933825         0    0.235805   0.238531   
imag       0.314653     0.234498  0.970727  0.235805           0  0.0212784   
detect     0.315289     0.235503  0.969044  0.238531   0.0212784          0   
model      0.415425      0.34556  0.999353  0.372166    0.238385   0.242376   
dataset    0.315423     0.234798  0.970931  0.237099   0.0137386  0.0135129   
learn      0.283802     0.201823  0.976742   0.21057   0.0449231  0.0450191   
pneumonia   0.31535     0.234737   0.97064   0.23636  0.00752893  0.0224521   
use        0.321572     0.244481  0.964258  0.249204    0.041137  0.0381077   
class      0.315121     0.234694  0.971307   0.23663   0.0098868  0.0185928   
chest             0            0         0         0           0          0   

              model    dataset      learn   pneumonia          use  \
covid       1.02773    0.98695   0.990224    0.989015     0.989909   
pandem      1.01363   0.984041   0.977253    0.983459      0.98432   
health     0.971414    0.93458   0.933886    0.934593     0.935058   
polici     0.593183   0.488377   0.454435    0.488653     0.497721   
impact     0.551071   0.636966   0.635038    0.634424     0.632756   
countri    0.867328   0.840011    0.83892    0.838624     0.835079   
develop    0.415425   0.315423   0.283802     0.31535     0.321572   
nation      0.34556   0.234798   0.201823    0.234737     0.244481   
psycholog  0.999353   0.970931   0.976742     0.97064     0.964258   
sustain    0.372166   0.237099    0.21057     0.23636     0.249204   
imag       0.238385  0.0137386  0.0449231  0.00752893     0.041137   
detect     0.242376  0.0135129  0.0450191   0.0224521    0.0381077   
model             0   0.240337   0.248378    0.236498      0.23267   
dataset    0.240337          0  0.0419004    0.014285    0.0402872   
learn      0.248378  0.0419004          0   0.0463739    0.0597224   
pneumonia  0.236498   0.014285  0.0463739           0     0.044148   
use         0.23267  0.0402872  0.0597224    0.044148  9.31323e-10   
class      0.238992  0.0121158  0.0434638  0.00980811    0.0442678   
chest             0          0          0           0            0   

                class chest  
covid        0.989045     0  
pandem       0.984206     0  
health       0.933089     0  
polici       0.488437     0  
impact       0.636346     0  
countri      0.839838     0  
develop      0.315121     0  
nation       0.234694     0  
psycholog    0.971307     0  
sustain       0.23663     0  
imag        0.0098868     0  
detect      0.0185928     0  
model        0.238992     0  
dataset     0.0121158     0  
learn       0.0434638     0  
pneumonia  0.00980811     0  
use         0.0442678     0  
class               0     0  
chest               0     0  

(-0.2627615739738422, 0.8836, -1.2038818741062816)

mantel_test_n(5)

Pairwise-distance
             trial     vaccin      stage     expect dose     covid      imag  \
trial            0  0.0154886  0.0255324  0.0187806    0  0.989816  0.981963   
vaccin   0.0154886          0  0.0303668   0.029283    0  0.980472   0.98415   
stage    0.0255324  0.0303668          0  0.0290166    0  0.989095  0.978101   
expect   0.0187806   0.029283  0.0290166          0    0  0.989618  0.983301   
dose             0          0          0          0    0         0         0   
covid     0.989816   0.980472   0.989095   0.989618    0         0   1.39644   
imag      0.981963    0.98415   0.978101   0.983301    0   1.39644         0   
detect    0.846972   0.850873   0.849099   0.847821    0   1.29896   1.30243   
model     0.986677   0.987979   0.985134   0.982803    0   1.39867   1.39232   
dataset   0.985212   0.985167   0.990273   0.980556    0   1.39968   1.39618   

           detect        model      dataset  
trial    0.846972     0.986677     0.985212  
vaccin   0.850873     0.987979     0.985167  
stage    0.849099     0.985134     0.990273  
expect   0.847821     0.982803     0.980556  
dose            0            0            0  
covid     1.29896      1.39867      1.39968  
imag      1.30243      1.39232      1.39618  
detect          0      1.29656       1.3039  
model     1.29656  1.49012e-08       1.3952  
dataset    1.3039       1.3952  1.49012e-08  
Pairwise-distance
            trial    vaccin     stage    expect      dose     covid imag  \
trial           0  0.763213  0.191796  0.306748  0.375118    1.0694    0   
vaccin   0.763213         0   0.69832  0.666691  0.580776   1.15955    0   
stage    0.191796   0.69832         0  0.139381   0.22782  0.996742    0   
expect   0.306748  0.666691  0.139381         0   0.15735  0.964262    0   
dose     0.375118  0.580776   0.22782   0.15735         0  0.954288    0   
covid      1.0694   1.15955  0.996742  0.964262  0.954288         0    0   
imag            0         0         0         0         0         0    0   
detect   0.518665  0.696521  0.361955  0.260061  0.234321  0.935885    0   
model    0.523859  0.678103  0.369097  0.266096  0.235633  0.934462    0   
dataset         0         0         0         0         0         0    0   

            detect      model dataset  
trial     0.518665   0.523859       0  
vaccin    0.696521   0.678103       0  
stage     0.361955   0.369097       0  
expect    0.260061   0.266096       0  
dose      0.234321   0.235633       0  
covid     0.935885   0.934462       0  
imag             0          0       0  
detect           0  0.0435761       0  
model    0.0435761          0       0  
dataset          0          0       0  

(-0.1169138190537623, 0.65842, -0.4078013989965543)

mantel_test_n(10)

Pairwise-distance
                trial     vaccin      stage     expect dose underway  \
trial               0  0.0154886  0.0255324  0.0187806    0        0   
vaccin      0.0154886          0  0.0303668   0.029283    0        0   
stage       0.0255324  0.0303668          0  0.0290166    0        0   
expect      0.0187806   0.029283  0.0290166          0    0        0   
dose                0          0          0          0    0        0   
underway            0          0          0          0    0        0   
novemb     0.00841559  0.0182124  0.0261269  0.0202633    0        0   
addit       0.0364459  0.0400624   0.041101  0.0370752    0        0   
develop     0.0462419  0.0418069  0.0524836  0.0485678    0        0   
covid        0.989816   0.980472   0.989095   0.989618    0        0   
imag         0.981963    0.98415   0.978101   0.983301    0        0   
detect       0.846972   0.850873   0.849099   0.847821    0        0   
model        0.986677   0.987979   0.985134   0.982803    0        0   
dataset      0.985212   0.985167   0.990273   0.980556    0        0   
learn        0.945584   0.944774   0.940107   0.945457    0        0   
pneumonia    0.790535   0.793378   0.791871   0.788024    0        0   
use          0.961232   0.962969   0.966376   0.961257    0        0   
class        0.956811   0.958604   0.959756   0.955272    0        0   
chest        0.179027   0.179391   0.184918   0.183565    0        0   

               novemb        addit    develop     covid      imag    detect  \
trial      0.00841559    0.0364459  0.0462419  0.989816  0.981963  0.846972   
vaccin      0.0182124    0.0400624  0.0418069  0.980472   0.98415  0.850873   
stage       0.0261269     0.041101  0.0524836  0.989095  0.978101  0.849099   
expect      0.0202633    0.0370752  0.0485678  0.989618  0.983301  0.847821   
dose                0            0          0         0         0         0   
underway            0            0          0         0         0         0   
novemb              0    0.0367017  0.0441295  0.990609  0.981931  0.846395   
addit       0.0367017  6.58545e-10  0.0466966  0.987194  0.983328  0.848089   
develop     0.0441295    0.0466966          0  0.981158  0.980472  0.852867   
covid        0.990609     0.987194   0.981158         0   1.39644   1.29896   
imag         0.981931     0.983328   0.980472   1.39644         0   1.30243   
detect       0.846395     0.848089   0.852867   1.29896   1.30243         0   
model        0.986638     0.985446   0.973088   1.39867   1.39232   1.29656   
dataset      0.986598     0.988547   0.981582   1.39968   1.39618    1.3039   
learn        0.945316     0.950861   0.941637   1.37238   1.35922   1.30405   
pneumonia    0.789666     0.796326   0.797802   1.26607   1.26861   1.13873   
use          0.961382     0.954611   0.947366   1.38383   1.38249   1.30539   
class        0.956261     0.953209   0.960884   1.37578   1.37136   1.28796   
chest        0.178066     0.183614   0.170227  0.985739  0.882261  0.783617   

                 model      dataset     learn pneumonia       use     class  \
trial         0.986677     0.985212  0.945584  0.790535  0.961232  0.956811   
vaccin        0.987979     0.985167  0.944774  0.793378  0.962969  0.958604   
stage         0.985134     0.990273  0.940107  0.791871  0.966376  0.959756   
expect        0.982803     0.980556  0.945457  0.788024  0.961257  0.955272   
dose                 0            0         0         0         0         0   
underway             0            0         0         0         0         0   
novemb        0.986638     0.986598  0.945316  0.789666  0.961382  0.956261   
addit         0.985446     0.988547  0.950861  0.796326  0.954611  0.953209   
develop       0.973088     0.981582  0.941637  0.797802  0.947366  0.960884   
covid          1.39867      1.39968   1.37238   1.26607   1.38383   1.37578   
imag           1.39232      1.39618   1.35922   1.26861   1.38249   1.37136   
detect         1.29656       1.3039   1.30405   1.13873   1.30539   1.28796   
model      1.49012e-08       1.3952   1.36274   1.26832   1.37644   1.37834   
dataset         1.3952  1.49012e-08   1.36981   1.25933   1.38163   1.37785   
learn          1.36274      1.36981         0    1.2345   1.36296   1.35307   
pneumonia      1.26832      1.25933    1.2345         0   1.23553   1.23926   
use            1.37644      1.38163   1.36296   1.23553         0   1.36188   
class          1.37834      1.37785   1.35307   1.23926   1.36188         0   
chest          1.00431     0.973249  0.970311  0.811805  0.899522   1.00454   

              chest  
trial      0.179027  
vaccin     0.179391  
stage      0.184918  
expect     0.183565  
dose              0  
underway          0  
novemb     0.178066  
addit      0.183614  
develop    0.170227  
covid      0.985739  
imag       0.882261  
detect     0.783617  
model       1.00431  
dataset    0.973249  
learn      0.970311  
pneumonia  0.811805  
use        0.899522  
class       1.00454  
chest             0  
Pairwise-distance
              trial    vaccin     stage     expect      dose     underway  \
trial             0  0.763213  0.191796   0.306748  0.375118     0.315692   
vaccin     0.763213         0   0.69832   0.666691  0.580776     0.672037   
stage      0.191796   0.69832         0   0.139381   0.22782     0.151689   
expect     0.306748  0.666691  0.139381          0   0.15735    0.0940441   
dose       0.375118  0.580776   0.22782    0.15735         0     0.152011   
underway   0.315692  0.672037  0.151689  0.0940441  0.152011  3.72529e-09   
novemb      0.36436  0.694497   0.21014   0.163107  0.195467     0.128686   
addit      0.371404  0.693585   0.21401   0.151799  0.189091     0.143363   
develop    0.439085  0.630086  0.300088   0.220954  0.187509     0.198019   
covid        1.0694   1.15955  0.996742   0.964262  0.954288     0.951555   
imag              0         0         0          0         0            0   
detect     0.518665  0.696521  0.361955   0.260061  0.234321     0.215186   
model      0.523859  0.678103  0.369097   0.266096  0.235633     0.225048   
dataset           0         0         0          0         0            0   
learn      0.517415  0.695166  0.360817   0.257937  0.229932     0.217792   
pneumonia   0.51784  0.702595  0.362218   0.259494  0.236501     0.220627   
use        0.517986  0.701514  0.361334   0.259183  0.230126     0.219444   
class      0.520249  0.699389  0.365241   0.264646  0.237546     0.221805   
chest             0         0         0          0         0            0   

             novemb     addit   develop     covid imag     detect      model  \
trial       0.36436  0.371404  0.439085    1.0694    0   0.518665   0.523859   
vaccin     0.694497  0.693585  0.630086   1.15955    0   0.696521   0.678103   
stage       0.21014   0.21401  0.300088  0.996742    0   0.361955   0.369097   
expect     0.163107  0.151799  0.220954  0.964262    0   0.260061   0.266096   
dose       0.195467  0.189091  0.187509  0.954288    0   0.234321   0.235633   
underway   0.128686  0.143363  0.198019  0.951555    0   0.215186   0.225048   
novemb            0  0.183012   0.21011  0.956536    0   0.219769   0.227201   
addit      0.183012         0  0.220307   0.95295    0   0.231125    0.23317   
develop     0.21011  0.220307         0   0.92692    0   0.204312   0.196099   
covid      0.956536   0.95295   0.92692         0    0   0.935885   0.934462   
imag              0         0         0         0    0          0          0   
detect     0.219769  0.231125  0.204312  0.935885    0          0  0.0435761   
model      0.227201   0.23317  0.196099  0.934462    0  0.0435761          0   
dataset           0         0         0         0    0          0          0   
learn       0.22019   0.22145   0.19819  0.929744    0  0.0321995  0.0362161   
pneumonia  0.220794  0.229239  0.191862  0.927594    0  0.0413219  0.0495457   
use        0.220409  0.224542   0.19429  0.909991    0  0.0487727  0.0508595   
class      0.225566  0.239112  0.209032  0.931289    0  0.0604411  0.0704158   
chest             0         0         0         0    0          0          0   

          dataset      learn  pneumonia          use        class chest  
trial           0   0.517415    0.51784     0.517986     0.520249     0  
vaccin          0   0.695166   0.702595     0.701514     0.699389     0  
stage           0   0.360817   0.362218     0.361334     0.365241     0  
expect          0   0.257937   0.259494     0.259183     0.264646     0  
dose            0   0.229932   0.236501     0.230126     0.237546     0  
underway        0   0.217792   0.220627     0.219444     0.221805     0  
novemb          0    0.22019   0.220794     0.220409     0.225566     0  
addit           0    0.22145   0.229239     0.224542     0.239112     0  
develop         0    0.19819   0.191862      0.19429     0.209032     0  
covid           0   0.929744   0.927594     0.909991     0.931289     0  
imag            0          0          0            0            0     0  
detect          0  0.0321995  0.0413219    0.0487727    0.0604411     0  
model           0  0.0362161  0.0495457    0.0508595    0.0704158     0  
dataset         0          0          0            0            0     0  
learn           0          0  0.0337346     0.029245     0.064926     0  
pneumonia       0  0.0337346          0    0.0431737    0.0731034     0  
use             0   0.029245  0.0431737  4.65661e-10    0.0624004     0  
class           0   0.064926  0.0731034    0.0624004  1.31709e-09     0  
chest           0          0          0            0            0     0  

(-0.045269414483095086, 0.60068, -0.2343293791362985)

def mantel_test_verticle(model_a,model_b,n):
    words_science = dict(model_a.show_topic(0, topn=word_count_science))
    words_psy = dict(model_b.show_topic(0, topn=word_count_psy))

    top_words_science = dict(model_a.show_topic(0, topn=n)) 
    top_words_psy = dict(model_b.show_topic(0,topn=n)) 


    c={} #law words in non law
    for key in top_words_science:
        try:
            c[key] = words_psy[key]
        except KeyError:
            c[key] = 0

    top_words_psy.update(c)

    b={} #non law words in law
    for key in top_words_psy:
        try:
            b[key] = words_science[key]
        except KeyError:
            b[key] = 0

    b.update(top_words_science)
    top_words_science=b

    #pairwise distance for law
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_science =dict(model_a.show_topic(i, topn=word_count_science))
        b=pd.DataFrame(words_from_science,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_science.keys()), 
                      index = list(top_words_science.keys()))

    l = list(top_words_science.keys())

    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df1 = df.to_numpy()
    #pairwise distace for non-law
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_psy =dict(model_b.show_topic(i, topn=word_count_psy))
        b=pd.DataFrame(words_from_psy,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_psy.keys()), 
                      index = list(top_words_psy.keys()))

    l = list(top_words_psy.keys())
    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df2 = df.to_numpy()
    

    dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])

    

    dist2 = list(df2[np.triu_indices(len(top_words_psy), k = 1)])

    return mantel.test(dist1, dist2, perms=10000, method='pearson', tail='upper')

mantel_test_verticle(model_science_articles,model_science_p,5)

Pairwise-distance
              support    granada     covid  research   programm      imag  \
support   1.86265e-09   0.103484   0.66527  0.156447   0.101063  0.834777   
granada      0.103484          0  0.701122  0.129845  0.0340417  0.816705   
covid         0.66527   0.701122         0  0.664301   0.704825   1.01386   
research     0.156447   0.129845  0.664301         0   0.119473  0.813048   
programm     0.101063  0.0340417  0.704825  0.119473          0  0.813252   
imag         0.834777   0.816705   1.01386  0.813048   0.813252         0   
model        0.730458   0.710287  0.966408  0.721914   0.709588   1.13159   
dataset      0.627463   0.626551  0.983197  0.625169   0.630951  0.935924   
class         0.49842   0.477425  0.747855  0.528585   0.496518   0.94137   

                model   dataset        class  
support      0.730458  0.627463      0.49842  
granada      0.710287  0.626551     0.477425  
covid        0.966408  0.983197     0.747855  
research     0.721914  0.625169     0.528585  
programm     0.709588  0.630951     0.496518  
imag          1.13159  0.935924      0.94137  
model     1.49012e-08  0.950057     0.851355  
dataset      0.950057         0     0.788631  
class        0.851355  0.788631  1.05367e-08  
Pairwise-distance
           support   granada     covid   research   programm      imag  \
support          0  0.532112   1.98901   0.421236   0.421236   1.59634   
granada   0.532112         0   1.49778   0.245104   0.245104   1.06422   
covid      1.98901   1.49778         0    1.72413    1.72413  0.718751   
research  0.421236  0.245104   1.72413  1.666e-08  1.666e-08   1.23459   
programm  0.421236  0.245104   1.72413  1.666e-08  1.666e-08   1.23459   
imag       1.59634   1.06422  0.718751    1.23459    1.23459         0   
model      1.33937  0.842472   1.19674   0.927949   0.927949  0.490208   
dataset    1.33937  0.842472   1.19674   0.927949   0.927949  0.490208   
class            0         0         0          0          0         0   

             model   dataset class  
support    1.33937   1.33937     0  
granada   0.842472  0.842472     0  
covid      1.19674   1.19674     0  
research  0.927949  0.927949     0  
programm  0.927949  0.927949     0  
imag      0.490208  0.490208     0  
model            0         0     0  
dataset          0         0     0  
class            0         0     0  

(0.28155964005516, 0.1844, 1.0448006280884938)

mantel_test_verticle(model_science_articles,model_science_paragraph,5)

Pairwise-distance
            covid      imag    detect        model   dataset        class
covid           0   1.01386  0.744875     0.966408  0.983197     0.747855
imag      1.01386         0  0.836253      1.13159  0.935924      0.94137
detect   0.744875  0.836253         0     0.721344   0.75098     0.605089
model    0.966408   1.13159  0.721344  1.49012e-08  0.950057     0.851355
dataset  0.983197  0.935924   0.75098     0.950057         0     0.788631
class    0.747855   0.94137  0.605089     0.851355  0.788631  1.05367e-08
Pairwise-distance
           covid     imag   detect    model  dataset        class
covid          0  1.39641  1.30231  1.39852  1.39979      1.37625
imag     1.39641        0  1.30502  1.39268  1.39636      1.37191
detect   1.30231  1.30502        0  1.29843  1.30473      1.28984
model    1.39852  1.39268  1.29843        0  1.39529       1.3792
dataset  1.39979  1.39636  1.30473  1.39529        0      1.37866
class    1.37625  1.37191  1.28984   1.3792  1.37866  1.49012e-08

(0.7846258052940326, 0.029166666666666667, 1.8380929187635267)

mantel_test_verticle(model_science_p,model_science_paragraph,5)

Pairwise-distance
             covid      imag    detect     model   dataset   support  \
covid            0  0.718751   1.19674   1.19674   1.19674   1.98901   
imag      0.718751         0  0.490208  0.490208  0.490208   1.59634   
detect     1.19674  0.490208         0         0         0   1.33937   
model      1.19674  0.490208         0         0         0   1.33937   
dataset    1.19674  0.490208         0         0         0   1.33937   
support    1.98901   1.59634   1.33937   1.33937   1.33937         0   
granada    1.49778   1.06422  0.842472  0.842472  0.842472  0.532112   
research   1.72413   1.23459  0.927949  0.927949  0.927949  0.421236   
programm   1.72413   1.23459  0.927949  0.927949  0.927949  0.421236   

           granada   research   programm  
covid      1.49778    1.72413    1.72413  
imag       1.06422    1.23459    1.23459  
detect    0.842472   0.927949   0.927949  
model     0.842472   0.927949   0.927949  
dataset   0.842472   0.927949   0.927949  
support   0.532112   0.421236   0.421236  
granada          0   0.245104   0.245104  
research  0.245104  1.666e-08  1.666e-08  
programm  0.245104  1.666e-08  1.666e-08  
Pairwise-distance
             covid      imag    detect     model   dataset    support  \
covid            0   1.39641   1.30231   1.39852   1.39979   0.986363   
imag       1.39641         0   1.30502   1.39268   1.39636   0.981402   
detect     1.30231   1.30502         0   1.29843   1.30473   0.853901   
model      1.39852   1.39268   1.29843         0   1.39529   0.983886   
dataset    1.39979   1.39636   1.30473   1.39529         0   0.984225   
support   0.986363  0.981402  0.853901  0.983886  0.984225          0   
granada    0.99046  0.981935   0.84969  0.986828  0.986814  0.0257449   
research  0.988272  0.984109  0.842649  0.988493  0.987892   0.037191   
programm  0.990405  0.981778  0.849816  0.986544  0.986564  0.0252031   

              granada     research    programm  
covid         0.99046     0.988272    0.990405  
imag         0.981935     0.984109    0.981778  
detect        0.84969     0.842649    0.849816  
model        0.986828     0.988493    0.986544  
dataset      0.986814     0.987892    0.986564  
support     0.0257449     0.037191   0.0252031  
granada   5.82077e-11    0.0492703  0.00342387  
research    0.0492703  9.31323e-10   0.0504024  
programm   0.00342387    0.0504024           0  

(0.26279745058340426, 0.1414, 1.173127795612967)

mantel_test_verticle(model_psy_articles,model_psy_p,5)

Pairwise-distance
           misinfodem        covid    articl    commun    health psycholog  \
misinfodem          0     0.646964  0.356566  0.221824  0.700337  0.630503   
covid        0.646964  1.49012e-08  0.580969  0.622214  0.898325  0.737681   
articl       0.356566     0.580969         0  0.325056  0.661872  0.515819   
commun       0.221824     0.622214  0.325056         0  0.662091  0.506073   
health       0.700337     0.898325  0.661872  0.662091         0  0.666982   
psycholog    0.630503     0.737681  0.515819  0.506073  0.666982         0   
social       0.513122     0.778668  0.565552    0.5067  0.798641   0.65021   
epidem       0.548041     0.779457   0.52647  0.523629  0.797412  0.633251   

                 social    epidem  
misinfodem     0.513122  0.548041  
covid          0.778668  0.779457  
articl         0.565552   0.52647  
commun           0.5067  0.523629  
health         0.798641  0.797412  
psycholog       0.65021  0.633251  
social      1.05367e-08  0.500852  
epidem         0.500852         0  
Pairwise-distance
           misinfodem     covid    articl    commun    health psycholog  \
misinfodem          0  0.605022  0.938588    1.3363    1.3363   1.15404   
covid        0.605022         0  0.507819  0.747682  0.747682  0.761729   
articl       0.938588  0.507819         0  0.549558  0.549558   0.25391   
commun         1.3363  0.747682  0.549558  8.33e-09         0  0.605737   
health         1.3363  0.747682  0.549558         0         0  0.605737   
psycholog     1.15404  0.761729   0.25391  0.605737  0.605737         0   
social        1.81507   1.21004   1.09912  0.549558  0.549558   1.12845   
epidem              0         0         0         0         0         0   

              social epidem  
misinfodem   1.81507      0  
covid        1.21004      0  
articl       1.09912      0  
commun      0.549558      0  
health      0.549558      0  
psycholog    1.12845      0  
social             0      0  
epidem             0      0  

(-0.1229249158139808, 0.6418, -0.3898907841370266)

mantel_test_verticle(model_psy_articles,model_psy_paragraph,5)

Pairwise-distance
                 covid       pandem    health    polici    impact psycholog  \
covid      1.49012e-08     0.586377  0.898325  0.600431   0.62641  0.737681   
pandem        0.586377  1.05367e-08  0.657499   0.38949  0.455361   0.55745   
health        0.898325     0.657499         0  0.689582  0.679214  0.666982   
polici        0.600431      0.38949  0.689582         0  0.288467  0.499608   
impact         0.62641     0.455361  0.679214  0.288467         0  0.556516   
psycholog     0.737681      0.55745  0.666982  0.499608  0.556516         0   
social        0.778668     0.639384  0.798641  0.547895  0.660645   0.65021   
epidem        0.779457     0.640981  0.797412  0.486348   0.58212  0.633251   

                social    epidem  
covid         0.778668  0.779457  
pandem        0.639384  0.640981  
health        0.798641  0.797412  
polici        0.547895  0.486348  
impact        0.660645   0.58212  
psycholog      0.65021  0.633251  
social     1.05367e-08  0.500852  
epidem        0.500852         0  
Pairwise-distance
             covid   pandem       health       polici    impact psycholog  \
covid            0  1.39456       1.3668      1.09939   1.15594   1.38989   
pandem     1.39456        0      1.36184      1.08269   1.17701   1.38531   
health      1.3668  1.36184  1.49012e-08      1.03367   1.15175   1.34992   
polici     1.09939  1.08269      1.03367  1.05367e-08  0.791091   1.09415   
impact     1.15594  1.17701      1.15175     0.791091         0   1.10981   
psycholog  1.38989  1.38531      1.34992      1.09415   1.10981         0   
social     1.36311  1.35974      1.32767      1.05558   1.14058   1.34864   
epidem     1.32028  1.31741      1.27611     0.983429   1.11027   1.29639   

            social       epidem  
covid      1.36311      1.32028  
pandem     1.35974      1.31741  
health     1.32767      1.27611  
polici     1.05558     0.983429  
impact     1.14058      1.11027  
psycholog  1.34864      1.29639  
social           0      1.26134  
epidem     1.26134  2.10734e-08  

(0.6582634123012504, 0.0169, 1.9312972812416624)

mantel_test_verticle(model_psy_p,model_psy_paragraph,5)

Pairwise-distance
               covid    pandem    health polici    impact misinfodem  \
covid              0   1.21004  0.747682      0  0.999968   0.605022   
pandem       1.21004         0  0.549558      0  0.846734    1.81507   
health      0.747682  0.549558         0      0   0.46139     1.3363   
polici             0         0         0      0         0          0   
impact      0.999968  0.846734   0.46139      0         0    1.49652   
misinfodem  0.605022   1.81507    1.3363      0   1.49652          0   
articl      0.507819   1.09912  0.549558      0  0.559313   0.938588   
commun      0.747682  0.549558         0      0   0.46139     1.3363   

              articl    commun  
covid       0.507819  0.747682  
pandem       1.09912  0.549558  
health      0.549558         0  
polici             0         0  
impact      0.559313   0.46139  
misinfodem  0.938588    1.3363  
articl             0  0.549558  
commun      0.549558  8.33e-09  
Pairwise-distance
               covid    pandem       health       polici    impact  \
covid              0   1.39456       1.3668      1.09939   1.15594   
pandem       1.39456         0      1.36184      1.08269   1.17701   
health        1.3668   1.36184  1.49012e-08      1.03367   1.15175   
polici       1.09939   1.08269      1.03367  1.05367e-08  0.791091   
impact       1.15594   1.17701      1.15175     0.791091         0   
misinfodem  0.980348   0.97737     0.938893      0.49231  0.636562   
articl       1.15683   1.15681        1.123     0.789341  0.957158   
commun      0.984013  0.995388     0.894287     0.511381  0.646779   

             misinfodem    articl     commun  
covid          0.980348   1.15683   0.984013  
pandem          0.97737   1.15681   0.995388  
health         0.938893     1.123   0.894287  
polici          0.49231  0.789341   0.511381  
impact         0.636562  0.957158   0.646779  
misinfodem  6.58545e-10   0.61746  0.0808268  
articl          0.61746         0   0.621029  
commun        0.0808268  0.621029          0  

(0.024257150643832416, 0.481, 0.06582798273513495)

mantel_test_verticle(model_science_articles,model_science_p,10)

Pairwise-distance
              support    granada     covid    programm  research      tabik  \
support   1.86265e-09   0.103484   0.66527    0.101063  0.156447   0.112256   
granada      0.103484          0  0.701122   0.0340417  0.129845  0.0255313   
covid         0.66527   0.701122         0    0.704825  0.664301    0.69942   
programm     0.101063  0.0340417  0.704825           0  0.119473   0.059573   
research     0.156447   0.129845  0.664301    0.119473         0   0.142549   
tabik        0.112256  0.0255313   0.69942    0.059573  0.142549          0   
garcía       0.100744  0.0197854  0.702338   0.0165103  0.123104  0.0447392   
decsai         0.1006  0.0255313  0.703747  0.00851043  0.121256  0.0510626   
project      0.139803   0.112686  0.717977    0.093771   0.14191   0.130951   
chart       0.0942134  0.0307356  0.701703   0.0151091   0.12225  0.0547991   
imag         0.834777   0.816705   1.01386    0.813252  0.813048   0.820213   
model        0.730458   0.710287  0.966408    0.709588  0.721914   0.711879   
dataset      0.627463   0.626551  0.983197    0.630951  0.625169   0.624449   
class         0.49842   0.477425  0.747855    0.496518  0.528585    0.46423   
learn        0.593029   0.575125  0.853802    0.570105  0.589986   0.580174   
perform      0.375731   0.384777  0.731261    0.371509  0.390496   0.396358   
train         0.38586   0.355405  0.687667     0.34612  0.346306   0.364306   
detect       0.402128   0.387824  0.744875    0.374346  0.392639    0.39954   
use          0.199238   0.242432  0.638247    0.236194   0.27416   0.250067   

               garcía      decsai      project        chart      imag  \
support      0.100744      0.1006     0.139803    0.0942134  0.834777   
granada     0.0197854   0.0255313     0.112686    0.0307356  0.816705   
covid        0.702338    0.703747     0.717977     0.701703   1.01386   
programm    0.0165103  0.00851043     0.093771    0.0151091  0.813252   
research     0.123104    0.121256      0.14191      0.12225  0.813048   
tabik       0.0447392   0.0510626     0.130951    0.0547991  0.820213   
garcía    6.58545e-10   0.0092209    0.0966177    0.0180033  0.814555   
decsai      0.0092209           0    0.0977345    0.0137877  0.813983   
project     0.0966177   0.0977345  1.31709e-09     0.100056  0.802624   
chart       0.0180033   0.0137877     0.100056  4.65661e-10  0.815584   
imag         0.814555    0.813983     0.802624     0.815584         0   
model        0.710159     0.70961     0.724079     0.712527   1.13159   
dataset      0.629463    0.629682      0.62359     0.630055  0.935924   
class        0.483911    0.491594     0.479794     0.494809   0.94137   
learn        0.572391    0.571174     0.575182     0.572531   0.99937   
perform      0.377898     0.37458     0.379545     0.373464  0.875433   
train        0.351227    0.348153     0.363336     0.352225  0.733119   
detect       0.379267    0.377473     0.368021     0.379922  0.836253   
use          0.238681    0.237311     0.250887     0.230908  0.829421   

                model   dataset        class     learn   perform        train  \
support      0.730458  0.627463      0.49842  0.593029  0.375731      0.38586   
granada      0.710287  0.626551     0.477425  0.575125  0.384777     0.355405   
covid        0.966408  0.983197     0.747855  0.853802  0.731261     0.687667   
programm     0.709588  0.630951     0.496518  0.570105  0.371509      0.34612   
research     0.721914  0.625169     0.528585  0.589986  0.390496     0.346306   
tabik        0.711879  0.624449      0.46423  0.580174  0.396358     0.364306   
garcía       0.710159  0.629463     0.483911  0.572391  0.377898     0.351227   
decsai        0.70961  0.629682     0.491594  0.571174   0.37458     0.348153   
project      0.724079   0.62359     0.479794  0.575182  0.379545     0.363336   
chart        0.712527  0.630055     0.494809  0.572531  0.373464     0.352225   
imag          1.13159  0.935924      0.94137   0.99937  0.875433     0.733119   
model     1.49012e-08  0.950057     0.851355  0.890738  0.595767     0.723461   
dataset      0.950057         0     0.788631  0.882097  0.729462     0.754527   
class        0.851355  0.788631  1.05367e-08  0.726501  0.654866     0.591455   
learn        0.890738  0.882097     0.726501         0  0.629472     0.603452   
perform      0.595767  0.729462     0.654866  0.629472         0     0.442139   
train        0.723461  0.754527     0.591455  0.603452  0.442139  7.45058e-09   
detect       0.721344   0.75098     0.605089  0.659699  0.472107     0.446377   
use          0.749587  0.656879     0.547929  0.578637   0.39981     0.438878   

            detect       use  
support   0.402128  0.199238  
granada   0.387824  0.242432  
covid     0.744875  0.638247  
programm  0.374346  0.236194  
research  0.392639   0.27416  
tabik      0.39954  0.250067  
garcía    0.379267  0.238681  
decsai    0.377473  0.237311  
project   0.368021  0.250887  
chart     0.379922  0.230908  
imag      0.836253  0.829421  
model     0.721344  0.749587  
dataset    0.75098  0.656879  
class     0.605089  0.547929  
learn     0.659699  0.578637  
perform   0.472107   0.39981  
train     0.446377  0.438878  
detect           0  0.418005  
use       0.418005         0  
Pairwise-distance
           support   granada     covid   programm   research     tabik  \
support          0  0.532112   1.98901   0.421236   0.421236  0.726469   
granada   0.532112         0   1.49778   0.245104   0.245104  0.210618   
covid      1.98901   1.49778         0    1.72413    1.72413   1.38111   
programm  0.421236  0.245104   1.72413  1.666e-08  1.666e-08  0.356721   
research  0.421236  0.245104   1.72413  1.666e-08  1.666e-08  0.356721   
tabik     0.726469  0.210618   1.38111   0.356721   0.356721         0   
garcía     1.06422  0.532112    1.0473   0.713441   0.713441  0.356721   
decsai    0.631854  0.285696   1.61677   0.210618   0.210618  0.245104   
project   0.631854  0.285696   1.61677   0.210618   0.210618  0.245104   
chart     0.927949  0.421236   1.28854   0.532112   0.532112  0.210618   
imag       1.59634   1.06422  0.718751    1.23459    1.23459   0.88115   
model      1.33937  0.842472   1.19674   0.927949   0.927949  0.631854   
dataset    1.33937  0.842472   1.19674   0.927949   0.927949  0.631854   
class            0         0         0          0          0         0   
learn      1.33937  0.842472   1.19674   0.927949   0.927949  0.631854   
perform          0         0         0          0          0         0   
train            0         0         0          0          0         0   
detect     1.33937  0.842472   1.19674   0.927949   0.927949  0.631854   
use        1.33937  0.842472   1.19674   0.927949   0.927949  0.631854   

            garcía    decsai   project     chart      imag       model  \
support    1.06422  0.631854  0.631854  0.927949   1.59634     1.33937   
granada   0.532112  0.285696  0.285696  0.421236   1.06422    0.842472   
covid       1.0473   1.61677   1.61677   1.28854  0.718751     1.19674   
programm  0.713441  0.210618  0.210618  0.532112   1.23459    0.927949   
research  0.713441  0.210618  0.210618  0.532112   1.23459    0.927949   
tabik     0.356721  0.245104  0.245104  0.210618   0.88115    0.631854   
garcía           0   0.57471   0.57471  0.245104  0.532112    0.438051   
decsai     0.57471         0         0  0.356721   1.07016    0.726469   
project    0.57471         0         0  0.356721   1.07016    0.726469   
chart     0.245104  0.356721  0.356721         0  0.713441    0.421236   
imag      0.532112   1.07016   1.07016  0.713441         0    0.490208   
model     0.438051  0.726469  0.726469  0.421236  0.490208           0   
dataset   0.438051  0.726469  0.726469  0.421236  0.490208           0   
class            0         0         0         0         0           0   
learn     0.438051  0.726469  0.726469  0.421236  0.490208  2.9451e-09   
perform          0         0         0         0         0           0   
train            0         0         0         0         0           0   
detect    0.438051  0.726469  0.726469  0.421236  0.490208           0   
use       0.438051  0.726469  0.726469  0.421236  0.490208           0   

             dataset class       learn perform train    detect       use  
support      1.33937     0     1.33937       0     0   1.33937   1.33937  
granada     0.842472     0    0.842472       0     0  0.842472  0.842472  
covid        1.19674     0     1.19674       0     0   1.19674   1.19674  
programm    0.927949     0    0.927949       0     0  0.927949  0.927949  
research    0.927949     0    0.927949       0     0  0.927949  0.927949  
tabik       0.631854     0    0.631854       0     0  0.631854  0.631854  
garcía      0.438051     0    0.438051       0     0  0.438051  0.438051  
decsai      0.726469     0    0.726469       0     0  0.726469  0.726469  
project     0.726469     0    0.726469       0     0  0.726469  0.726469  
chart       0.421236     0    0.421236       0     0  0.421236  0.421236  
imag        0.490208     0    0.490208       0     0  0.490208  0.490208  
model              0     0  2.9451e-09       0     0         0         0  
dataset            0     0  2.9451e-09       0     0         0         0  
class              0     0           0       0     0         0         0  
learn     2.9451e-09     0           0       0     0         0         0  
perform            0     0           0       0     0         0         0  
train              0     0           0       0     0         0         0  
detect             0     0           0       0     0         0         0  
use                0     0           0       0     0         0         0  

(0.18832783456653382, 0.1602, 1.0344855963798059)

mantel_test_verticle(model_science_articles,model_science_paragraph,10)

Pairwise-distance
              covid      imag    detect        model   dataset     learn  \
covid             0   1.01386  0.744875     0.966408  0.983197  0.853802   
imag        1.01386         0  0.836253      1.13159  0.935924   0.99937   
detect     0.744875  0.836253         0     0.721344   0.75098  0.659699   
model      0.966408   1.13159  0.721344  1.49012e-08  0.950057  0.890738   
dataset    0.983197  0.935924   0.75098     0.950057         0  0.882097   
learn      0.853802   0.99937  0.659699     0.890738  0.882097         0   
pneumonia  0.773571  0.758103  0.546756     0.754637  0.795041  0.668047   
use        0.638247  0.829421  0.418005     0.749587  0.656879  0.578637   
class      0.747855   0.94137  0.605089     0.851355  0.788631  0.726501   
chest      0.709922  0.764922  0.377088     0.749506  0.723607  0.632363   
perform    0.731261  0.875433  0.472107     0.595767  0.729462  0.629472   
train      0.687667  0.733119  0.446377     0.723461  0.754527  0.603452   

          pneumonia       use        class     chest   perform        train  
covid      0.773571  0.638247     0.747855  0.709922  0.731261     0.687667  
imag       0.758103  0.829421      0.94137  0.764922  0.875433     0.733119  
detect     0.546756  0.418005     0.605089  0.377088  0.472107     0.446377  
model      0.754637  0.749587     0.851355  0.749506  0.595767     0.723461  
dataset    0.795041  0.656879     0.788631  0.723607  0.729462     0.754527  
learn      0.668047  0.578637     0.726501  0.632363  0.629472     0.603452  
pneumonia         0  0.494211     0.589365  0.366109  0.404197      0.41413  
use        0.494211         0     0.547929  0.318827   0.39981     0.438878  
class      0.589365  0.547929  1.05367e-08  0.517852  0.654866     0.591455  
chest      0.366109  0.318827     0.517852         0  0.432931     0.382922  
perform    0.404197   0.39981     0.654866  0.432931         0     0.442139  
train       0.41413  0.438878     0.591455  0.382922  0.442139  7.45058e-09  
Pairwise-distance
              covid      imag   detect    model   dataset    learn pneumonia  \
covid             0   1.39641  1.30231  1.39852   1.39979   1.3732    1.2644   
imag        1.39641         0  1.30502  1.39268   1.39636  1.35923   1.26827   
detect      1.30231   1.30502        0  1.29843   1.30473  1.30442   1.13954   
model       1.39852   1.39268  1.29843        0   1.39529  1.36244   1.26609   
dataset     1.39979   1.39636  1.30473  1.39529         0  1.36983   1.25795   
learn        1.3732   1.35923  1.30442  1.36244   1.36983        0    1.2337   
pneumonia    1.2644   1.26827  1.13954  1.26609   1.25795   1.2337         0   
use         1.38431   1.38285  1.30798  1.37699   1.38172  1.36356    1.2342   
class       1.37625   1.37191  1.28984   1.3792   1.37866  1.35238   1.23597   
chest      0.986319  0.882962  0.78581   1.0041  0.973256  0.97086  0.804832   
perform        1.36   1.35253  1.27706  1.36312   1.36501  1.33536   1.22927   
train       1.37996   1.37372  1.27963  1.36895   1.37946  1.34356   1.24062   

                use        class        chest   perform     train  
covid       1.38431      1.37625     0.986319      1.36   1.37996  
imag        1.38285      1.37191     0.882962   1.35253   1.37372  
detect      1.30798      1.28984      0.78581   1.27706   1.27963  
model       1.37699       1.3792       1.0041   1.36312   1.36895  
dataset     1.38172      1.37866     0.973256   1.36501   1.37946  
learn       1.36356      1.35238      0.97086   1.33536   1.34356  
pneumonia    1.2342      1.23597     0.804832   1.22927   1.24062  
use               0      1.36196     0.898382   1.35353   1.36356  
class       1.36196  1.49012e-08      1.00948   1.37043   1.35461  
chest      0.898382      1.00948  3.72529e-09  0.962046  0.978063  
perform     1.35353      1.37043     0.962046         0   1.34856  
train       1.36356      1.35461     0.978063   1.34856         0  

(0.4851744054306096, 0.0043, 1.6749964469443324)

mantel_test_verticle(model_science_p,model_science_paragraph,10)

Pairwise-distance
              covid      imag    detect       model     dataset       learn  \
covid             0  0.718751   1.19674     1.19674     1.19674     1.19674   
imag       0.718751         0  0.490208    0.490208    0.490208    0.490208   
detect      1.19674  0.490208         0           0           0           0   
model       1.19674  0.490208         0           0           0  2.9451e-09   
dataset     1.19674  0.490208         0           0           0  2.9451e-09   
learn       1.19674  0.490208         0  2.9451e-09  2.9451e-09           0   
pneumonia         0         0         0           0           0           0   
use         1.19674  0.490208         0           0           0           0   
class             0         0         0           0           0           0   
chest      0.956202  0.245104  0.245104    0.245104    0.245104    0.245104   
support     1.98901   1.59634   1.33937     1.33937     1.33937     1.33937   
granada     1.49778   1.06422  0.842472    0.842472    0.842472    0.842472   
programm    1.72413   1.23459  0.927949    0.927949    0.927949    0.927949   
research    1.72413   1.23459  0.927949    0.927949    0.927949    0.927949   
tabik       1.38111   0.88115  0.631854    0.631854    0.631854    0.631854   
garcía       1.0473  0.532112  0.438051    0.438051    0.438051    0.438051   
decsai      1.61677   1.07016  0.726469    0.726469    0.726469    0.726469   
project     1.61677   1.07016  0.726469    0.726469    0.726469    0.726469   
chart       1.28854  0.713441  0.421236    0.421236    0.421236    0.421236   

          pneumonia       use class     chest   support   granada   programm  \
covid             0   1.19674     0  0.956202   1.98901   1.49778    1.72413   
imag              0  0.490208     0  0.245104   1.59634   1.06422    1.23459   
detect            0         0     0  0.245104   1.33937  0.842472   0.927949   
model             0         0     0  0.245104   1.33937  0.842472   0.927949   
dataset           0         0     0  0.245104   1.33937  0.842472   0.927949   
learn             0         0     0  0.245104   1.33937  0.842472   0.927949   
pneumonia         0         0     0         0         0         0          0   
use               0         0     0  0.245104   1.33937  0.842472   0.927949   
class             0         0     0         0         0         0          0   
chest             0  0.245104     0         0   1.45294  0.927949    1.06422   
support           0   1.33937     0   1.45294         0  0.532112   0.421236   
granada           0  0.842472     0  0.927949  0.532112         0   0.245104   
programm          0  0.927949     0   1.06422  0.421236  0.245104  1.666e-08   
research          0  0.927949     0   1.06422  0.421236  0.245104  1.666e-08   
tabik             0  0.631854     0  0.726469  0.726469  0.210618   0.356721   
garcía            0  0.438051     0  0.421236   1.06422  0.532112   0.713441   
decsai            0  0.726469     0   0.88115  0.631854  0.285696   0.210618   
project           0  0.726469     0   0.88115  0.631854  0.285696   0.210618   
chart             0  0.421236     0  0.532112  0.927949  0.421236   0.532112   

            research     tabik    garcía    decsai   project     chart  
covid        1.72413   1.38111    1.0473   1.61677   1.61677   1.28854  
imag         1.23459   0.88115  0.532112   1.07016   1.07016  0.713441  
detect      0.927949  0.631854  0.438051  0.726469  0.726469  0.421236  
model       0.927949  0.631854  0.438051  0.726469  0.726469  0.421236  
dataset     0.927949  0.631854  0.438051  0.726469  0.726469  0.421236  
learn       0.927949  0.631854  0.438051  0.726469  0.726469  0.421236  
pneumonia          0         0         0         0         0         0  
use         0.927949  0.631854  0.438051  0.726469  0.726469  0.421236  
class              0         0         0         0         0         0  
chest        1.06422  0.726469  0.421236   0.88115   0.88115  0.532112  
support     0.421236  0.726469   1.06422  0.631854  0.631854  0.927949  
granada     0.245104  0.210618  0.532112  0.285696  0.285696  0.421236  
programm   1.666e-08  0.356721  0.713441  0.210618  0.210618  0.532112  
research   1.666e-08  0.356721  0.713441  0.210618  0.210618  0.532112  
tabik       0.356721         0  0.356721  0.245104  0.245104  0.210618  
garcía      0.713441  0.356721         0   0.57471   0.57471  0.245104  
decsai      0.210618  0.245104   0.57471         0         0  0.356721  
project     0.210618  0.245104   0.57471         0         0  0.356721  
chart       0.532112  0.210618  0.245104  0.356721  0.356721         0  
Pairwise-distance
              covid      imag    detect     model   dataset     learn  \
covid             0   1.39641   1.30231   1.39852   1.39979    1.3732   
imag        1.39641         0   1.30502   1.39268   1.39636   1.35923   
detect      1.30231   1.30502         0   1.29843   1.30473   1.30442   
model       1.39852   1.39268   1.29843         0   1.39529   1.36244   
dataset     1.39979   1.39636   1.30473   1.39529         0   1.36983   
learn        1.3732   1.35923   1.30442   1.36244   1.36983         0   
pneumonia    1.2644   1.26827   1.13954   1.26609   1.25795    1.2337   
use         1.38431   1.38285   1.30798   1.37699   1.38172   1.36356   
class       1.37625   1.37191   1.28984    1.3792   1.37866   1.35238   
chest      0.986319  0.882962   0.78581    1.0041  0.973256   0.97086   
support    0.986363  0.981402  0.853901  0.983886  0.984225  0.943631   
granada     0.99046  0.981935   0.84969  0.986828  0.986814  0.945935   
programm   0.990405  0.981778  0.849816  0.986544  0.986564  0.945813   
research   0.988272  0.984109  0.842649  0.988493  0.987892  0.945296   
tabik        0.9847  0.978188  0.853353  0.988694  0.979246  0.948068   
garcía     0.990397  0.981822  0.849818  0.986589  0.986647  0.945807   
decsai       0.9904  0.981816  0.849852  0.986571  0.986637  0.945778   
project    0.991427  0.980895  0.851088  0.984798  0.985759  0.946591   
chart      0.990449  0.981861  0.850215  0.986663  0.986645  0.946181   

          pneumonia       use        class        chest    support  \
covid        1.2644   1.38431      1.37625     0.986319   0.986363   
imag        1.26827   1.38285      1.37191     0.882962   0.981402   
detect      1.13954   1.30798      1.28984      0.78581   0.853901   
model       1.26609   1.37699       1.3792       1.0041   0.983886   
dataset     1.25795   1.38172      1.37866     0.973256   0.984225   
learn        1.2337   1.36356      1.35238      0.97086   0.943631   
pneumonia         0    1.2342      1.23597     0.804832   0.794866   
use          1.2342         0      1.36196     0.898382    0.95857   
class       1.23597   1.36196  1.49012e-08      1.00948   0.960527   
chest      0.804832  0.898382      1.00948  3.72529e-09   0.180226   
support    0.794866   0.95857     0.960527     0.180226          0   
granada    0.787564  0.961854     0.957397     0.179676  0.0257449   
programm   0.787657  0.961582     0.957668     0.179829  0.0252031   
research   0.789753   0.96739     0.960694     0.179993   0.037191   
tabik      0.792418  0.963592     0.959968     0.175947  0.0364283   
garcía     0.787483  0.961656     0.957368      0.17988  0.0258964   
decsai     0.787451  0.961623     0.957407     0.179875  0.0258657   
project    0.789346  0.961949     0.953392      0.18313  0.0216227   
chart      0.787586  0.961595     0.957501     0.179951  0.0246206   

               granada     programm     research        tabik       garcía  \
covid          0.99046     0.990405     0.988272       0.9847     0.990397   
imag          0.981935     0.981778     0.984109     0.978188     0.981822   
detect         0.84969     0.849816     0.842649     0.853353     0.849818   
model         0.986828     0.986544     0.988493     0.988694     0.986589   
dataset       0.986814     0.986564     0.987892     0.979246     0.986647   
learn         0.945935     0.945813     0.945296     0.948068     0.945807   
pneumonia     0.787564     0.787657     0.789753     0.792418     0.787483   
use           0.961854     0.961582      0.96739     0.963592     0.961656   
class         0.957397     0.957668     0.960694     0.959968     0.957368   
chest         0.179676     0.179829     0.179993     0.175947      0.17988   
support      0.0257449    0.0252031     0.037191    0.0364283    0.0258964   
granada    5.82077e-11   0.00342387    0.0492703    0.0247978   0.00328537   
programm    0.00342387            0    0.0504024    0.0243051  0.000991496   
research     0.0492703    0.0504024  9.31323e-10    0.0589419    0.0508115   
tabik        0.0247978    0.0243051    0.0589419  4.65661e-10    0.0243088   
garcía      0.00328537  0.000991496    0.0508115    0.0243088            0   
decsai      0.00322982  0.000998245    0.0508298    0.0244051  0.000239281   
project      0.0140737    0.0134268    0.0476632    0.0307116    0.0135392   
chart       0.00421459   0.00259535    0.0494682    0.0238276   0.00284113   

                decsai    project        chart  
covid           0.9904   0.991427     0.990449  
imag          0.981816   0.980895     0.981861  
detect        0.849852   0.851088     0.850215  
model         0.986571   0.984798     0.986663  
dataset       0.986637   0.985759     0.986645  
learn         0.945778   0.946591     0.946181  
pneumonia     0.787451   0.789346     0.787586  
use           0.961623   0.961949     0.961595  
class         0.957407   0.953392     0.957501  
chest         0.179875    0.18313     0.179951  
support      0.0258657  0.0216227    0.0246206  
granada     0.00322982  0.0140737   0.00421459  
programm   0.000998245  0.0134268   0.00259535  
research     0.0508298  0.0476632    0.0494682  
tabik        0.0244051  0.0307116    0.0238276  
garcía     0.000239281  0.0135392   0.00284113  
decsai               0  0.0135462   0.00290882  
project      0.0135462          0    0.0132016  
chart       0.00290882  0.0132016  5.82077e-11  

(-0.008107041561424666, 0.5304, -0.051907664378004925)

mantel_test_verticle(model_psy_articles,model_psy_p,10)

Pairwise-distance
           misinfodem        covid    articl    commun    health     mechan  \
misinfodem          0     0.646964  0.356566  0.221824  0.700337   0.193529   
covid        0.646964  1.49012e-08  0.580969  0.622214  0.898325   0.608591   
articl       0.356566     0.580969         0  0.325056  0.661872   0.228043   
commun       0.221824     0.622214  0.325056         0  0.662091   0.172176   
health       0.700337     0.898325  0.661872  0.662091         0   0.666958   
mechan       0.193529     0.608591  0.228043  0.172176  0.666958          0   
psycholog    0.630503     0.737681  0.515819  0.506073  0.666982   0.532973   
strategi     0.174144     0.617777  0.254271  0.155585  0.647421  0.0847199   
misinform    0.103669     0.623656  0.279462  0.170592  0.675438  0.0914923   
public       0.362985     0.663889  0.416788   0.26013  0.671538   0.328671   
social       0.513122     0.778668  0.565552    0.5067  0.798641   0.490075   
epidem       0.548041     0.779457   0.52647  0.523629  0.797412   0.461706   
pandem         0.4723     0.586377  0.368308  0.466977  0.657499   0.412763   
group        0.843878     0.977762   0.77328  0.843095   1.03874   0.785871   
peopl        0.579953     0.786247  0.526517  0.533434    0.8931   0.527273   
studi        0.430426     0.521524  0.423924  0.388305  0.765614   0.335305   
categori     0.407967     0.708764  0.364137   0.36565  0.714492   0.299364   

           psycholog   strategi  misinform    public       social    epidem  \
misinfodem  0.630503   0.174144   0.103669  0.362985     0.513122  0.548041   
covid       0.737681   0.617777   0.623656  0.663889     0.778668  0.779457   
articl      0.515819   0.254271   0.279462  0.416788     0.565552   0.52647   
commun      0.506073   0.155585   0.170592   0.26013       0.5067  0.523629   
health      0.666982   0.647421   0.675438  0.671538     0.798641  0.797412   
mechan      0.532973  0.0847199  0.0914923  0.328671     0.490075  0.461706   
psycholog          0   0.536779   0.570775   0.51661      0.65021  0.633251   
strategi    0.536779          0  0.0929785  0.329351     0.500749  0.491073   
misinform   0.570775  0.0929785          0  0.331209     0.487018  0.486127   
public       0.51661   0.329351   0.331209         0     0.533862  0.581191   
social       0.65021   0.500749   0.487018  0.533862  1.05367e-08  0.500852   
epidem      0.633251   0.491073   0.486127  0.581191     0.500852         0   
pandem       0.55745   0.439484   0.432643  0.548831     0.639384  0.640981   
group       0.939908    0.80032   0.810428  0.910733      0.84304  0.934338   
peopl       0.712449   0.553789   0.545219  0.494831     0.571794  0.723179   
studi       0.540289   0.361623   0.371373  0.525322     0.566199  0.442122   
categori    0.555281   0.326897    0.33187  0.467141     0.470567   0.25915   

                 pandem     group     peopl        studi     categori  
misinfodem       0.4723  0.843878  0.579953     0.430426     0.407967  
covid          0.586377  0.977762  0.786247     0.521524     0.708764  
articl         0.368308   0.77328  0.526517     0.423924     0.364137  
commun         0.466977  0.843095  0.533434     0.388305      0.36565  
health         0.657499   1.03874    0.8931     0.765614     0.714492  
mechan         0.412763  0.785871  0.527273     0.335305     0.299364  
psycholog       0.55745  0.939908  0.712449     0.540289     0.555281  
strategi       0.439484   0.80032  0.553789     0.361623     0.326897  
misinform      0.432643  0.810428  0.545219     0.371373      0.33187  
public         0.548831  0.910733  0.494831     0.525322     0.467141  
social         0.639384   0.84304  0.571794     0.566199     0.470567  
epidem         0.640981  0.934338  0.723179     0.442122      0.25915  
pandem      1.05367e-08  0.902724  0.610391     0.519571     0.504374  
group          0.902724         0  0.822353     0.801354     0.837159  
peopl          0.610391  0.822353         0       0.6697     0.627939  
studi          0.519571  0.801354    0.6697  5.26836e-09     0.406992  
categori       0.504374  0.837159  0.627939     0.406992  5.26836e-09  
Pairwise-distance
           misinfodem     covid    articl      commun      health      mechan  \
misinfodem          0  0.605022  0.938588      1.3363      1.3363      1.3363   
covid        0.605022         0  0.507819    0.747682    0.747682    0.747682   
articl       0.938588  0.507819         0    0.549558    0.549558    0.549558   
commun         1.3363  0.747682  0.549558    8.33e-09           0  5.8902e-09   
health         1.3363  0.747682  0.549558           0           0  5.8902e-09   
mechan         1.3363  0.747682  0.549558  5.8902e-09  5.8902e-09           0   
psycholog     1.15404  0.761729   0.25391    0.605737    0.605737    0.605737   
strategi      1.15404  0.761729   0.25391    0.605737    0.605737    0.605737   
misinform     1.61339   1.00837  0.919963    0.376149    0.376149    0.376149   
public        1.30501  0.769359  0.403348    0.249613    0.249613    0.249613   
social        1.81507   1.21004   1.09912    0.549558    0.549558    0.549558   
epidem              0         0         0           0           0           0   
pandem        1.81507   1.21004   1.09912    0.549558    0.549558    0.549558   
group               0         0         0           0           0           0   
peopl         1.30501  0.769359  0.403348    0.249613    0.249613    0.249613   
studi               0         0         0           0           0           0   
categori            0         0         0           0           0           0   

           psycholog  strategi misinform    public    social epidem    pandem  \
misinfodem   1.15404   1.15404   1.61339   1.30501   1.81507      0   1.81507   
covid       0.761729  0.761729   1.00837  0.769359   1.21004      0   1.21004   
articl       0.25391   0.25391  0.919963  0.403348   1.09912      0   1.09912   
commun      0.605737  0.605737  0.376149  0.249613  0.549558      0  0.549558   
health      0.605737  0.605737  0.376149  0.249613  0.549558      0  0.549558   
mechan      0.605737  0.605737  0.376149  0.249613  0.549558      0  0.549558   
psycholog          0         0  0.976987  0.376149   1.12845      0   1.12845   
strategi           0         0  0.976987  0.376149   1.12845      0   1.12845   
misinform   0.976987  0.976987         0  0.605737  0.201674      0  0.201674   
public      0.376149  0.376149  0.605737         0  0.752298      0  0.752298   
social       1.12845   1.12845  0.201674  0.752298         0      0         0   
epidem             0         0         0         0         0      0         0   
pandem       1.12845   1.12845  0.201674  0.752298         0      0         0   
group              0         0         0         0         0      0         0   
peopl       0.376149  0.376149  0.605737         0  0.752298      0  0.752298   
studi              0         0         0         0         0      0         0   
categori           0         0         0         0         0      0         0   

           group     peopl studi categori  
misinfodem     0   1.30501     0        0  
covid          0  0.769359     0        0  
articl         0  0.403348     0        0  
commun         0  0.249613     0        0  
health         0  0.249613     0        0  
mechan         0  0.249613     0        0  
psycholog      0  0.376149     0        0  
strategi       0  0.376149     0        0  
misinform      0  0.605737     0        0  
public         0         0     0        0  
social         0  0.752298     0        0  
epidem         0         0     0        0  
pandem         0  0.752298     0        0  
group          0         0     0        0  
peopl          0         0     0        0  
studi          0         0     0        0  
categori       0         0     0        0  

(-0.2297680221676044, 0.873, -1.1783474351270118)

mantel_test_verticle(model_psy_articles,model_psy_paragraph,10)

Pairwise-distance
                 covid       pandem    health    polici    impact   countri  \
covid      1.49012e-08     0.586377  0.898325  0.600431   0.62641  0.518814   
pandem        0.586377  1.05367e-08  0.657499   0.38949  0.455361  0.557135   
health        0.898325     0.657499         0  0.689582  0.679214  0.843713   
polici        0.600431      0.38949  0.689582         0  0.288467  0.471694   
impact         0.62641     0.455361  0.679214  0.288467         0  0.563248   
countri       0.518814     0.557135  0.843713  0.471694  0.563248         0   
develop       0.629306     0.336736  0.671243  0.175511   0.25525  0.496134   
nation        0.647706     0.440737  0.625509  0.199817  0.290865  0.489712   
psycholog     0.737681      0.55745  0.666982  0.499608  0.556516  0.629914   
sustain       0.663098     0.368456  0.732703  0.217227  0.233976  0.513263   
social        0.778668     0.639384  0.798641  0.547895  0.660645  0.684603   
epidem        0.779457     0.640981  0.797412  0.486348   0.58212  0.632774   
group         0.977762     0.902724   1.03874   0.80399  0.855776  0.940168   
peopl         0.786247     0.610391    0.8931   0.53792  0.630889  0.707097   
studi         0.521524     0.519571  0.765614  0.401643  0.460194  0.365929   
categori      0.708764     0.504374  0.714492  0.313374  0.464554  0.536335   

            develop    nation psycholog      sustain       social    epidem  \
covid      0.629306  0.647706  0.737681     0.663098     0.778668  0.779457   
pandem     0.336736  0.440737   0.55745     0.368456     0.639384  0.640981   
health     0.671243  0.625509  0.666982     0.732703     0.798641  0.797412   
polici     0.175511  0.199817  0.499608     0.217227     0.547895  0.486348   
impact      0.25525  0.290865  0.556516     0.233976     0.660645   0.58212   
countri    0.496134  0.489712  0.629914     0.513263     0.684603  0.632774   
develop           0  0.268647  0.493522      0.13975     0.559921  0.514662   
nation     0.268647         0  0.540401     0.298087     0.545452  0.496225   
psycholog  0.493522  0.540401         0     0.562132      0.65021  0.633251   
sustain     0.13975  0.298087  0.562132  5.26836e-09     0.617513   0.54609   
social     0.559921  0.545452   0.65021     0.617513  1.05367e-08  0.500852   
epidem     0.514662  0.496225  0.633251      0.54609     0.500852         0   
group      0.826579  0.814363  0.939908     0.838135      0.84304  0.934338   
peopl      0.539387  0.547083  0.712449     0.545505     0.571794  0.723179   
studi      0.431464  0.407736  0.540289      0.46265     0.566199  0.442122   
categori   0.371733  0.344302  0.555281     0.405575     0.470567   0.25915   

              group     peopl        studi     categori  
covid      0.977762  0.786247     0.521524     0.708764  
pandem     0.902724  0.610391     0.519571     0.504374  
health      1.03874    0.8931     0.765614     0.714492  
polici      0.80399   0.53792     0.401643     0.313374  
impact     0.855776  0.630889     0.460194     0.464554  
countri    0.940168  0.707097     0.365929     0.536335  
develop    0.826579  0.539387     0.431464     0.371733  
nation     0.814363  0.547083     0.407736     0.344302  
psycholog  0.939908  0.712449     0.540289     0.555281  
sustain    0.838135  0.545505      0.46265     0.405575  
social      0.84304  0.571794     0.566199     0.470567  
epidem     0.934338  0.723179     0.442122      0.25915  
group             0  0.822353     0.801354     0.837159  
peopl      0.822353         0       0.6697     0.627939  
studi      0.801354    0.6697  5.26836e-09     0.406992  
categori   0.837159  0.627939     0.406992  5.26836e-09  
Pairwise-distance
             covid   pandem       health       polici    impact   countri  \
covid            0  1.39456       1.3668      1.09939   1.15594   1.29102   
pandem     1.39456        0      1.36184      1.08269   1.17701   1.30974   
health      1.3668  1.36184  1.49012e-08      1.03367   1.15175    1.2532   
polici     1.09939  1.08269      1.03367  1.05367e-08  0.791091  0.910725   
impact     1.15594  1.17701      1.15175     0.791091         0   1.08197   
countri    1.29102  1.30974       1.2532     0.910725   1.08197         0   
develop    1.03181  1.05541     0.984009     0.205193  0.655298  0.813813   
nation     1.01093  1.01238     0.952938     0.264841  0.621691  0.843174   
psycholog  1.38989  1.38531      1.34992      1.09415   1.10981   1.28099   
sustain    1.00581  0.99898      0.96167     0.294896  0.628615  0.868165   
social     1.36311  1.35974      1.32767      1.05558   1.14058   1.26342   
epidem     1.32028  1.31741      1.27611     0.983429   1.11027   1.19881   
group      1.37552  1.37115      1.34083      1.07168   1.15586   1.27115   
peopl      1.39182  1.39188      1.36442      1.09022   1.18119   1.29454   
studi      1.38346  1.38002      1.33938      1.06783   1.10184   1.30625   
categori   1.06792  1.08108      1.02989     0.642902   0.83105  0.950291   

               develop    nation psycholog   sustain    social       epidem  \
covid          1.03181   1.01093   1.38989   1.00581   1.36311      1.32028   
pandem         1.05541   1.01238   1.38531   0.99898   1.35974      1.31741   
health        0.984009  0.952938   1.34992   0.96167   1.32767      1.27611   
polici        0.205193  0.264841   1.09415  0.294896   1.05558     0.983429   
impact        0.655298  0.621691   1.10981  0.628615   1.14058      1.11027   
countri       0.813813  0.843174   1.28099  0.868165   1.26342      1.19881   
develop    5.26836e-09  0.102428   1.01212  0.153274  0.989335      0.92602   
nation        0.102428         0   1.00742  0.116366  0.969206     0.900412   
psycholog      1.01212   1.00742         0  0.935989   1.34864      1.29639   
sustain       0.153274  0.116366  0.935989         0  0.964883     0.912774   
social        0.989335  0.969206   1.34864  0.964883         0      1.26134   
epidem         0.92602  0.900412   1.29639  0.912774   1.26134  2.10734e-08   
group         0.987056    0.9865   1.36412  0.965698   1.34392       1.2897   
peopl           1.0244    1.0042   1.37522   1.00625   1.36314       1.3125   
studi          1.02035  0.986848   1.37158  0.985974   1.34907      1.29755   
categori      0.528185  0.492811   1.06132  0.484499   1.04139      1.02249   

              group        peopl        studi  categori  
covid       1.37552      1.39182      1.38346   1.06792  
pandem      1.37115      1.39188      1.38002   1.08108  
health      1.34083      1.36442      1.33938   1.02989  
polici      1.07168      1.09022      1.06783  0.642902  
impact      1.15586      1.18119      1.10184   0.83105  
countri     1.27115      1.29454      1.30625  0.950291  
develop    0.987056       1.0244      1.02035  0.528185  
nation       0.9865       1.0042     0.986848  0.492811  
psycholog   1.36412      1.37522      1.37158   1.06132  
sustain    0.965698      1.00625     0.985974  0.484499  
social      1.34392      1.36314      1.34907   1.04139  
epidem       1.2897       1.3125      1.29755   1.02249  
group             0       1.3681      1.36193   1.04687  
peopl        1.3681  1.49012e-08      1.37339   1.07426  
studi       1.36193      1.37339  1.49012e-08   1.01089  
categori    1.04687      1.07426      1.01089         0  

(0.6923716848577194, 0.0004, 2.992341191219701)

mantel_test_verticle(model_psy_p,model_psy_paragraph,10)

Pairwise-distance
               covid    pandem      health polici    impact countri   develop  \
covid              0   1.21004    0.747682      0  0.999968       0  0.999968   
pandem       1.21004         0    0.549558      0  0.846734       0  0.846734   
health      0.747682  0.549558           0      0   0.46139       0   0.46139   
polici             0         0           0      0         0       0         0   
impact      0.999968  0.846734     0.46139      0         0       0         0   
countri            0         0           0      0         0       0         0   
develop     0.999968  0.846734     0.46139      0         0       0         0   
nation             0         0           0      0         0       0         0   
psycholog   0.761729   1.12845    0.605737      0  0.403348       0  0.403348   
sustain            0         0           0      0         0       0         0   
misinfodem  0.605022   1.81507      1.3363      0   1.49652       0   1.49652   
articl      0.507819   1.09912    0.549558      0  0.559313       0  0.559313   
commun      0.747682  0.549558           0      0   0.46139       0   0.46139   
mechan      0.747682  0.549558  5.8902e-09      0   0.46139       0   0.46139   
strategi    0.761729   1.12845    0.605737      0  0.403348       0  0.403348   
misinform    1.00837  0.201674    0.376149      0  0.748839       0  0.748839   
public      0.769359  0.752298    0.249613      0   0.25391       0   0.25391   

           nation psycholog sustain misinfodem    articl      commun  \
covid           0  0.761729       0   0.605022  0.507819    0.747682   
pandem          0   1.12845       0    1.81507   1.09912    0.549558   
health          0  0.605737       0     1.3363  0.549558           0   
polici          0         0       0          0         0           0   
impact          0  0.403348       0    1.49652  0.559313     0.46139   
countri         0         0       0          0         0           0   
develop         0  0.403348       0    1.49652  0.559313     0.46139   
nation          0         0       0          0         0           0   
psycholog       0         0       0    1.15404   0.25391    0.605737   
sustain         0         0       0          0         0           0   
misinfodem      0   1.15404       0          0  0.938588      1.3363   
articl          0   0.25391       0   0.938588         0    0.549558   
commun          0  0.605737       0     1.3363  0.549558    8.33e-09   
mechan          0  0.605737       0     1.3363  0.549558  5.8902e-09   
strategi        0         0       0    1.15404   0.25391    0.605737   
misinform       0  0.976987       0    1.61339  0.919963    0.376149   
public          0  0.376149       0    1.30501  0.403348    0.249613   

                mechan  strategi misinform    public  
covid         0.747682  0.761729   1.00837  0.769359  
pandem        0.549558   1.12845  0.201674  0.752298  
health      5.8902e-09  0.605737  0.376149  0.249613  
polici               0         0         0         0  
impact         0.46139  0.403348  0.748839   0.25391  
countri              0         0         0         0  
develop        0.46139  0.403348  0.748839   0.25391  
nation               0         0         0         0  
psycholog     0.605737         0  0.976987  0.376149  
sustain              0         0         0         0  
misinfodem      1.3363   1.15404   1.61339   1.30501  
articl        0.549558   0.25391  0.919963  0.403348  
commun      5.8902e-09  0.605737  0.376149  0.249613  
mechan               0  0.605737  0.376149  0.249613  
strategi      0.605737         0  0.976987  0.376149  
misinform     0.376149  0.976987         0  0.605737  
public        0.249613  0.376149  0.605737         0  
Pairwise-distance
               covid    pandem       health       polici    impact   countri  \
covid              0   1.39456       1.3668      1.09939   1.15594   1.29102   
pandem       1.39456         0      1.36184      1.08269   1.17701   1.30974   
health        1.3668   1.36184  1.49012e-08      1.03367   1.15175    1.2532   
polici       1.09939   1.08269      1.03367  1.05367e-08  0.791091  0.910725   
impact       1.15594   1.17701      1.15175     0.791091         0   1.08197   
countri      1.29102   1.30974       1.2532     0.910725   1.08197         0   
develop      1.03181   1.05541     0.984009     0.205193  0.655298  0.813813   
nation       1.01093   1.01238     0.952938     0.264841  0.621691  0.843174   
psycholog    1.38989   1.38531      1.34992      1.09415   1.10981   1.28099   
sustain      1.00581   0.99898      0.96167     0.294896  0.628615  0.868165   
misinfodem  0.980348   0.97737     0.938893      0.49231  0.636562  0.838812   
articl       1.15683   1.15681        1.123     0.789341  0.957158    1.0477   
commun      0.984013  0.995388     0.894287     0.511381  0.646779  0.857724   
mechan      0.987155  0.984927     0.934301     0.485861  0.638337  0.832962   
strategi    0.988771  0.982785     0.928748      0.48959  0.642035   0.82574   
misinform   0.986281  0.984316     0.928228     0.488185  0.640051  0.838083   
public       1.34577   1.34317      1.27098       1.0254   1.14947   1.23672   

                develop    nation psycholog   sustain   misinfodem    articl  \
covid           1.03181   1.01093   1.38989   1.00581     0.980348   1.15683   
pandem          1.05541   1.01238   1.38531   0.99898      0.97737   1.15681   
health         0.984009  0.952938   1.34992   0.96167     0.938893     1.123   
polici         0.205193  0.264841   1.09415  0.294896      0.49231  0.789341   
impact         0.655298  0.621691   1.10981  0.628615     0.636562  0.957158   
countri        0.813813  0.843174   1.28099  0.868165     0.838812    1.0477   
develop     5.26836e-09  0.102428   1.01212  0.153274      0.32039  0.704544   
nation         0.102428         0   1.00742  0.116366     0.246941  0.659286   
psycholog       1.01212   1.00742         0  0.935989     0.970974    1.1648   
sustain        0.153274  0.116366  0.935989         0     0.237685   0.69756   
misinfodem      0.32039  0.246941  0.970974  0.237685  6.58545e-10   0.61746   
articl         0.704544  0.659286    1.1648   0.69756      0.61746         0   
commun         0.342172  0.273573  0.978529  0.265863    0.0808268  0.621029   
mechan         0.312328  0.238978  0.971444  0.232141    0.0307227  0.623133   
strategi        0.31596  0.244931   0.97406  0.238283    0.0251287  0.620875   
misinform      0.316964  0.243373  0.974328  0.236585    0.0230978  0.622929   
public         0.959606  0.944074   1.32602  0.943731     0.878004   1.09759   

               commun     mechan   strategi  misinform       public  
covid        0.984013   0.987155   0.988771   0.986281      1.34577  
pandem       0.995388   0.984927   0.982785   0.984316      1.34317  
health       0.894287   0.934301   0.928748   0.928228      1.27098  
polici       0.511381   0.485861    0.48959   0.488185       1.0254  
impact       0.646779   0.638337   0.642035   0.640051      1.14947  
countri      0.857724   0.832962    0.82574   0.838083      1.23672  
develop      0.342172   0.312328    0.31596   0.316964     0.959606  
nation       0.273573   0.238978   0.244931   0.243373     0.944074  
psycholog    0.978529   0.971444    0.97406   0.974328      1.32602  
sustain      0.265863   0.232141   0.238283   0.236585     0.943731  
misinfodem  0.0808268  0.0307227  0.0251287  0.0230978     0.878004  
articl       0.621029   0.623133   0.620875   0.622929      1.09759  
commun              0  0.0965992  0.0792103  0.0817424     0.842543  
mechan      0.0965992          0  0.0253733  0.0256321     0.899125  
strategi    0.0792103  0.0253733          0   0.023209     0.884301  
misinform   0.0817424  0.0256321   0.023209          0     0.878599  
public       0.842543   0.899125   0.884301   0.878599  1.49012e-08  

(0.051967882478809554, 0.412, 0.25059387073785117)